Remote cleaning quality management systems and related methods of use

ABSTRACT

Embodiments of the present disclosure disclose a method for remotely managing a cleaning quality for an indoor location being cleaned. The method includes accessing a training dataset including a plurality of plot points and associated signal strengths of a predefined signal received from a fixed network device, where at least one plot point is preselected based on a predefined cleaning attribute associated with a physical spot corresponding to the at least one plot point; receiving the predefined signal at a position in the indoor location from the fixed network device, where the received signal has a second signal strength and the position is determined proximate to the plot point based on the second signal strength and each of the signal strengths; and calculating a cumulative duration spent at the determined position based on a predefined cleaning schedule to assess the cleaning quality for the physical spot.

TECHNICAL FIELD

The subject matter described herein generally relates to cleaningmanagement systems and particularly relates to remote cleaning qualitymanagement systems.

BACKGROUND

Everyone can appreciate a well-cleaned and well-organized facility ordwelling. In a commercial set-up, a clean and organized workspacepromotes health, morale, and productivity of its occupants. Suchworkspace also makes a great first impression on potential customers andvisitors, thereby boosting sales as well as brand image of an occupyingbusiness. Often a cleaning or janitorial staff is deployed to performvarious cleaning tasks such as garbage disposal, vacuum cleaning, wipingdust and stains from surfaces, replenishing consumables and utilityitems (e.g., pens, notepads, water bottles, coffee pods, etc.), andorganizing spatial items (e.g., furniture, communication equipment,etc.). The cleaning or janitorial staff is typically unskilled oruntrained and therefore, often require training through videos,site-specific cleaning demonstrations, on-the-job feedback, etc. toimprove their cleaning performance. Despite such trainings, the cleaningquality delivered by the staff invariably falls short of the expectedcleaning standard.

SUMMARY

One common approach to address the issue of substandard cleaning qualityincludes tracking a location of a cleaning staff using the globalpositioning system (GPS) or that inputted by the cleaning staff uponarrival at a geographical location such as an airport for an assignedwork shift. Additionally, the total time spent by the cleaning staff atthe geographical location is determined based on the clock times atwhich the staff arrives and leaves that location. Both the total timeand the geographical location (indicated by GPS coordinates) of thecleaning staff are typically used as a measure of cleaning quality,despite those defining mere availability of the cleaning staff at thegeographical location for the corresponding work shift. The cleaningquality measured by this approach often fails to indicate whethervarious indoor areas at the geographical location are attended for beingcleaned by the cleaning staff and is therefore inaccurate andunreliable. Moreover, GPS signals weaken through building structures andare unsuitable for indoor determination of the cleaning quality.

Another typical approach includes a checklist of cleaning tasks beingfilled-out by the cleaning staff or an inspection staff after the indoorareas are cleaned. The filled-out checklist is evaluated independentlyor in combination with various inputs (e.g., comments from the staff orthe customer, site photographs, etc.) based on physical inspections ofthe cleaned indoor areas to assess the cleaning quality. The filled-outchecklist is subject to inaccuracies due to the incorrect or variableunderstanding of the expected cleaning quality by the cleaning staff, orthe inspection staff, and is therefore unreliable. Additionally, thephysical inspections are time-consuming, cost-intensive, and unreliabledue to a difference in the level of experience and skill as well assusceptibility to bias of the inspection staff.

Yet another traditional approach includes indoor solutions formonitoring the cleaning or inspection staff through the indoor areas tobe cleaned. These indoor solutions typically require additional hardwaresuch as cameras and/or active radiofrequency beacons to be physicallyinstalled at various indoor areas, thereby magnifying the system andoperational costs. Moreover, similar to the GPS-based approach, thesesolutions determine the presence and therefore, mere availability, ofthe cleaning or inspection staff at those indoor areas. Thesehardware-intensive solutions fail to determine the quality of cleaningperformed at various points in those indoor areas or whether suchcleaning quality meets the expected cleaning standard without thephysical inspections. Therefore, there exists a need for a simpler,robust, reliable, and economical solution for remote cleaning qualitymanagement.

One embodiment of the present disclosure includes a computer-implementedmethod for remotely managing a cleaning quality for an indoor locationbeing cleaned. The method may include accessing, using a remote cleaningquality management (RCQM) module on a computer with a processor and amemory, a training dataset including a plurality of plot points and oneor more signal strengths associated therewith of a predefined signalreceived from at least one spatially fixed network device. The pluralityof plot points may correspond to physical spots at the indoor location,where at least one plot point may be preselected from the plurality ofplot points based on a predefined cleaning attribute associated with aphysical spot corresponding to the at least one plot point. The methodalso includes receiving, using the RCQM module, the predefined signal ata position in the indoor location from the at least one spatially fixednetwork device. The received signal may have a second signal strengthgreater than a predefined signal threshold value, where the position maybe determined proximate to the at least one plot point based on thesecond signal strength in combination with each of the one or moresignal strengths. The method may further include calculating, using theRCQM module, a cumulative duration spent at the determined positionbased on a predefined cleaning schedule to assess a cleaning quality forthe physical spot. The cleaning quality may be assessed based on thecalculated cumulative duration being compared with a set of one or morepredefined time threshold values.

One aspect of the present disclosure includes providing, using an outputmodule on the computer in communication with the RCQM module, anindication based on the calculated cumulative duration exceeding apredefined time threshold value in the set of one or more predefinedtime threshold values.

Another aspect of the present disclosure includes the set of one or morepredefined time threshold values being relative to a total time spentproximate to at least one of (i) the physical spot, (ii) the indoorlocation, (iii) a geographical location indicating the indoor location,and any combinations thereof.

Yet another aspect of the present disclosure includes each of theplurality of plot points is a virtual reference point associated with afloor plan of the indoor location, where at least one of the pluralityof plot points is mapped on the floor plan relative to one or morepreliminary plot points, which are preassigned to the floor plan basedon physical characteristics of the indoor location, where the one ormore preliminary plot points assist in defining a virtual fenceproximate to the physical spot at the indoor location.

Still another aspect of the present disclosure includes the cleaningschedule having a predefined maximum duration for completing a cleaningtask within a preset period, where the predefined maximum duration maybe less than the preset period.

A further aspect of the present disclosure includes the cleaningattribute having at least one of (i) the cleaning schedule, (ii) acleaning task or a type thereof, (iii) a cleaning product, (iv) acleaning equipment, (v) a proximity of the physical spot from a user ora predefined area proximate to the indoor location, (vi) a clock time,and any combinations thereof.

Another aspect of the present disclosure includes the predefined signalthreshold value ranging from approximately −70 dBm to approximately −10dBm.

Yet another aspect of the present disclosure includes the predefinedsignal corresponding to at least one of a radiofrequency signal, a lightsignal, a sound signal, and any combinations thereof.

Still another aspect of the present disclosure includes the predefinedsignal being a Wi-Fi signal.

A further aspect of the present disclosure includes the cumulativeduration having a single duration or a sum of at least two temporallyseparate durations.

Another embodiment of the present disclosure may include a system forremotely managing a cleaning quality for an indoor location beingcleaned. The system may include a portable device capable of beingnavigated across one or more surfaces in the indoor location. Theportable device may be configured to: (1) access a training datasetincluding a plurality of plot points and one or more signal strengthsassociated therewith of a predefined signal received from at least onespatially fixed network device, where the plurality of plot pointscorresponds to physical spots at the indoor location; (2) receive thepredefined signal at a position in the indoor location from the at leastone spatially fixed network device, where the received signal has asecond signal strength greater than a predefined signal threshold value;and (3) calculate a cumulative duration at the position based on apredefined cleaning schedule. The system may also include a server incommunication with the portable device. The server may be configured to:(1) select at least one plot point from the plurality of plot pointsbased on a predefined cleaning attribute associated with a physical spotcorresponding to the at least one plot point; (2) determine the positionbeing proximate to the selected at least one plot point based on thesecond signal strength in combination with each of the one or moresignal strengths; and (3) assess a cleaning quality for the physicalspot based on the calculated cumulative duration at the determinedposition being compared with a set of one or more predefined timethreshold values, where a portion of the calculated cumulative durationexceeding a maximum time threshold value in the set is unaccountedtowards assessing the cleaning quality.

Another aspect of the present disclosure includes the server beingfurther configured to provide an indication based on the calculatedcumulative duration exceeding the maximum time threshold value.

The above summary of exemplary embodiments is not intended to describeeach disclosed embodiment or every implementation of the presentdisclosure. Other and further aspects and features of the disclosurewill be evident from reading the following detailed description of theembodiments, which are intended to illustrate, not limit, the presentdisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The illustrated embodiments of the subject matter will be bestunderstood by reference to drawings, wherein like parts are designatedby like numerals throughout. The following description is intended onlyby way of example, and simply illustrates certain selected embodimentsof systems and methods that are consistent with the subject matter asclaimed herein.

FIGS. 1-4 are schematics of network environments including an exemplaryremote cleaning quality management (RCQM) device, according toembodiments of the present disclosure.

FIG. 5 illustrates the exemplary RCQM device of FIGS. 1-4, according toan embodiment of present disclosure.

FIG. 6 illustrates an exemplary method for implementing an input moduleof the RCQM device of FIG. 5, according to an embodiment of the presentdisclosure.

FIG. 7 illustrates an exemplary method for implementing a plot trainingmodule of the RCQM device of FIG. 5, according to an embodiment of thepresent disclosure.

FIG. 8 illustrates an exemplary method for implementing an RCQM moduleof the RCQM device of FIG. 5, according to an embodiment of the presentdisclosure.

FIG. 9 illustrates an exemplary cleaning quality metric implemented bythe RCQM device of FIG. 5, according to an embodiment of the presentdisclosure.

FIG. 10 illustrates an exemplary floor plan indicative of a designatedphysical location for implementing the RCQM device of FIG. 5, accordingto an embodiment of the present disclosure.

FIG. 11 illustrates a portion of the floor plan of FIG. 10 includingexemplary preliminary plot points implemented using the RCQM device ofFIG. 5, according to an embodiment of the present disclosure.

FIG. 12 illustrates an overlay of a top view of a designated cleaninglocation on the floor plan of FIG. 11 including exemplary signal plotpoints implemented using the RCQM device of FIG. 5, according to anembodiment of the present disclosure.

FIG. 13 illustrates the overlay of FIG. 12 indicating a set of signalplot points selected for the RCQM device of FIG. 5, according to anembodiment of the present disclosure.

DETAILED DESCRIPTION

The following detailed description is provided with reference to thefigures. Exemplary embodiments are described to illustrate thedisclosure, not to limit its scope, which is defined by the claims.Those of ordinary skill in the art will recognize a number of equivalentvariations in the description that follows without departing from thescope and spirit of the disclosure.

Non-Limiting Definitions

Definitions of one or more terms that will be used in this disclosureare described below without limitations. For a person skilled in theart, it is understood that the definitions are provided just for thesake of clarity and are intended to include more examples than justprovided in the detailed description.

A “user” is used in the present disclosure within the context of itsbroadest definition. The user may refer to a person, a machine, anartificial intelligence unit, or any other entity, which may communicatewith one or more modules loaded or integrated with an electronic devicecapable of or configured to perform a specific function. The entity mayinclude a group of persons or organizations such as professionalservices organizations, product manufacturing organizations, financemanagement organizations, real estate organizations, marketing films,marketplaces, and so on that can operate online over a network.

A “cleaning task” or “cleaning,” including all its variations, are usedinterchangeably in the present disclosure within the context of itsbroadest definition. The cleaning may refer to an act, task, or statedirected towards (1) the prevention of spread of infections or diseases,(2) dust control, (3) preservation of fabrics, fixtures, fittings,furnishings, or similar, (4) a provision of an environment acceptablefor intended use in various settings such as social or businesssettings, and/or (5) safety.

A “designated physical location” is used in the present disclosurewithin the context of its broadest definition. The designated physicallocation may refer to an indoor location or a section proximate theretowithin a physical space represented by or indicative of a geographicallocation. In some cases, the designated physical location may representa sub-location within a predefined proximity of the geographicallocation.

A “designated cleaning location” is used in the present disclosurewithin the context of its broadest definition. The designated cleaninglocation may refer to a surface or a region of the designated physicallocation, or a portion proximate thereto, where the cleaning task isintended to be performed.

A “cleaning frequency” is used in the present disclosure within thecontext of its broadest definition. The cleaning frequency may refer tothe number of times the designated cleaning location is cleaned within apredefined period.

A “cleaning task repetition” is used in the present disclosure withinthe context of its broadest definition. The cleaning task repetition mayrefer to the number of times a specific cleaning task is performedindependently or in association with (1) another cleaning task, (2) acleaning entity (e.g., the user, a cleaning equipment, etc.), or (3) thecleaning frequency.

A “cleaning schedule” is used in the present disclosure within thecontext of its broadest definition. The cleaning schedule may refer to aset of at least one cleaning task and a maximum duration associatedtherewith for completing that cleaning task within a preset period. Insome cases, the cleaning schedule may include only a maximum durationavailable for cleaning the designated physical location, or a portionthereof, within the preset period. In some other cases, the presetperiod may be defined by set clock times.

A “cleaning quality” is used in the present disclosure within thecontext of its broadest definition. The cleaning quality may refer to adegree of cleanliness including spatial organization achieved uponcompletion of a single cleaning task or a set of cleaning tasks. Thedegree of cleanliness may be related to, without limitation, (1) thecleaning frequency; (2) the cleaning task repetition; (3) a skill,experience, or performance of the cleaning entity, (4) the cleaningtask, (5) an inspection of (a) the cleaning task, or an outcome thereof,and/or (b) the designated cleaning location; (6) a type of the cleaningentity, or technologies involved therewith; (7) an intended use of thedesignated cleaning location or any locations proximate thereto; (8) thecleaning schedule; (9) time-bound cleaning obligations or expectations;(9) socio-economic factors related to the designated physical locationor a location proximate thereto (e.g., type and frequency of use, brandvalue, a number of simultaneous users, etc.).

A “plot point” is used in the present disclosure within the context ofits broadest definition. The plot point may refer to a virtual referencepoint indicative of a physical spot at the designated physical locationor a portion thereof.

A “floor plan” is used in the present disclosure within the context ofits broadest definition. The floor plan may refer to a scale diagram,digital imagery, virtual model, mathematical representation, or anycombinations thereof, indicating a designated physical location or aportion thereof, and/or its relationship with other designated physicallocations or portions thereof.

A “signal plot plan” is used in the present disclosure within thecontext of its broadest definition. The signal plot plan may refer tothe floor plan including at least one plot point indicative of aphysical location of an entity receiving or providing signal attributes(e.g., signal strength; signal proximity from a signal provider such asan access point; regions of signal construction, attenuation, orinterference; time of flight; angle of arrival; etc.).

A “training dataset” is used in the present disclosure within thecontext of its broadest definition. The training dataset may refer to aset of one or more signal plot points and associated strength of signalsreceived at physical spots indicated by those signal plot points. Insome embodiments, the training dataset may also include additionalparameters (e.g., (i) relative proximity from predetermined networkdevices; (ii) media access control (MAC) address of the signal provider;(iii) associated geographical location coordinates, etc.) and valuesthereof.

A “stable signal” is used in the present disclosure within the contextof its broadest definition. The stable signal may refer to two or moresamples of a predefined signal (e.g., a radiofrequency signal, a lightsignal, a sound signal, etc.) having at least one aspect (e.g.,frequency, wavelength, signal strength or power, angle of arrival, timeof flight, etc.) at an acceptable value relative to a predeterminedsignal threshold value for a predefined amount of time when thepredefined signal is received by a predefined or dynamically defineddestination or entity. In some cases, the acceptable value of the atleast one aspect may allow the predefined signal to become detectable.In some other cases, the acceptable value may be equal to or greaterthan the predetermined signal threshold value.

A “scanning proximity” is used in the present disclosure within thecontext of its broadest definition. The scanning proximity may refer toa predetermined region proximate to an entity or a location where thestable signal is receivable.

Exemplary Embodiments

FIGS. 1-4 are schematics of network environments including an exemplaryremote cleaning quality management (RCQM) device, according toembodiments of the present disclosure. Embodiments are disclosed in thecontext of remotely managing the cleaning quality upon a cleaning taskbeing performed at a designated physical location such as an indoorlocation. However, some embodiments may be applied for (i) remotemanagement of a localized activity, (ii) time or priority management, or(iii) efficiency management at indoor and/or alfresco areas within aphysical space indicative of or represented by a geographical location(e.g., an airport) in the context of various business, social, andpersonal scenarios. Examples of such scenarios may include, but are notlimited to, item tracking through multiple checkpoints in factories,warehouses, garages, etc.; serving food at different tables in arestaurant; interactions of attendees with people, items, or events inconference halls, amusement parks, etc.; people meeting each otherduring speed dating or matchmaking events; shopping in malls; indoormarketing; staff or visitor management in fenced premises such as hotelsand airports; managing activities of drones, robots, or autonomousvehicles for intended use; neighborhood watch; observing behaviors ofanimals within designated spaces such as homes and animal shelters;determining seating preferences of different types of diners at cafésand patios associated therewith; and so on.

The illustrated embodiments (FIGS. 1-4) include an RCQM device 102 incommunication with one or more network devices such as a server 104 overa network 106. The network 106 may include any software, hardware, orcomputer applications capable of providing a medium to exchange signalsor data in any format known in the art, related art, or developed later.The network 106 may include, but is not limited to, social mediaplatforms implemented as a website, a unified communication application,or a standalone application. Examples of the social media platforms mayinclude, but are not limited to, Twitter™, Facebook™, Skype™, MicrosoftLync™, Cisco Webex™, and Google Hangouts™. Further, the network 106 mayinclude, for example, one or more of the Internet, Wide Area Networks(WANs), Local Area Networks (LANs), analog or digital wired and wirelesstelephone networks (e.g., a PSTN, Integrated Services Digital Network(ISDN), a cellular network, and Digital Subscriber Line (xDSL), Wi-Fi,radio, television, cable, satellite, and/or any other delivery ortunneling mechanism for carrying data. The network 106 may includemultiple networks or sub-networks, each of which may include, e.g., awired or wireless data pathway. The network 106 may include acircuit-switched voice network, a packet-switched data network, or anyother network configurable to carry electronic communications. Forexample, the network 106 may include networks based on the Internetprotocol (IP) or asynchronous transfer mode (ATM), and may support voiceusing, for example, VoIP, Voice-over-ATM, or other comparable protocolsused for voice, video, and data communications.

In a first exemplary embodiment (FIG. 1), the RCQM device 102 may beinstalled, integrated, or operatively associated with a user device 108,which may include any computing device known in the art, related art, ordeveloped later capable of being implemented, wholly or in-part, as amovable or portable device. Examples of the user device 108 may include,but are not limited to, a mobile computing device (e.g., a mobile phone,a tablet, a laptop, a smartwatch, etc.), a portable internet appliance,and powered or unpowered devices capable of being spatially navigated(e.g., a Segway, a wheelchair, a vacuum cleaner, a curing machine, adisinfection device, a standalone radiofrequency transceiver sticker,etc.). The RCQM device 102 may be preconfigured or dynamicallyconfigured to, at least one of, (1) communicate synchronously orasynchronously with one or more software applications, databases,storage devices, or appliances operating via same or differentcommunication protocols, formats, database schemas, platforms or anycombination thereof, to send and receive a variety of data; (2) collect,define, store, and analyze the data; (3) formulate one or more tasks forbeing performed on or trained from the data; (4) provide, execute,communicate, and/or assist in formulating one or more mathematicalmodels for tasks related to collection, identification, manipulation,and/or presentation of the data; (5) display, print, or communicate theidentified, manipulated, and/or presentable data; and (6) transfer ormap the models, tasks, parameters, attributes, and associated values ofthe data to one or more networked computing devices and/or datarepositories.

The RCQM device 102 may represent any of a wide variety of devicescapable of providing remote cleaning quality management services to thenetwork devices. Alternatively, the RCQM device 102 may be implementedas a software application or a device driver. The RCQM device 102 mayenhance or increase the functionality and/or capacity of the network,such as the network 106, to which it may be connected. In someembodiments, the RCQM device 102 may be also configured, for example, toperform notification tasks, security tasks, network management tasksincluding Internet protocol (IP) address management, and other tasks. Insome other embodiments, the RCQM device 102 may be further configured toexpose its computing environment or operating code to a user, and mayinclude related art input or output (I/O) devices, such as a keyboard, acamera, and a display device. The RCQM device 102 of some embodimentsmay, however, include software, firmware, or other resources thatsupport the remote administration, operation, power control, and/ormaintenance of the RCQM device 102.

In further embodiments, the RCQM device 102 either in communication withany of the network devices such as the user device 108, or dedicatedly,may have video, voice, or data communication capabilities (e.g., unifiedcommunication capabilities) by being coupled to or including, variousimaging devices (e.g., cameras, printers, scanners, medical imagingsystems, etc.), various audio devices (e.g., microphones, music players,recorders, audio input devices, speakers, audio output devices,telephones, speaker telephones, etc.), various video devices (e.g.,monitors, projectors, displays or display screens, televisions, videooutput devices, video input devices, camcorders, etc.), or any othertypes of hardware, in any combination thereof. In some embodiments, theRCQM device 102 may comprise or implement various real time protocolsand non-real-time protocols known in the art, related art, or developedlater to facilitate data transfer among the user device 108, the server104, and the RCQM device 102, or any other network devices. In someembodiments, the RCQM device 102 may be configured to convertcommunications, which may include instructions, queries, data, files,etc., from the user device 108 into appropriate formats to make suchcommunications compatible with the network devices (e.g., the server104, another RCQM device, etc.) and vice versa. Consequently, the RCQMdevice 102 may allow implementation of the network devices usingdifferent technologies or by different organizations, such as athird-party vendor, managing the server 104 or associated services basedon a proprietary technology.

A second embodiment (FIG. 2) may include an RCQM device 110-1 and anRCQM device 110-2 (collectively, referred to as the RCQM devices 110),each being similar to the RCQM device 102. The RCQM devices 110 may beoperatively coupled to each other and preconfigured or dynamicallyconfigured to interact with the server 104 over the network 106. Forexample, the RCQM device 110-1 may be installed on, integrated, oroperatively associated with the server 104, which may be implemented asany of a variety of computing devices including, for example,general-purpose computing devices, multiple networked servers (arrangedin clusters or as a server farm), a mainframe, or so forth. On the otherhand, the RCQM device 110-2 may be implemented as a standalone device incommunication with the RCQM device 110-1 via the server 104 on thenetwork 106.

Similarly, a third embodiment (FIG. 3) may include the RCQM device 110-1being integrated, installed, or operatively associated with a networkappliance 302 such as an access point configured to establish thenetwork 106 among the network devices such as the server 104 and theRCQM devices 110. At least one of the RCQM device 110-1 and the networkappliance 302 may be capable of operating as or providing an interfaceto assist the exchange of software instructions and data among thenetwork devices such as the server 104 and the RCQM devices 110. In someembodiments, the network appliance 302 may be preconfigured ordynamically configured to include the RCQM device 110-1 integrated withother devices. For example, the RCQM device 110-1 may be integrated withthe server 104 (as shown in FIG. 2) or any other computing deviceconnected to the network 106. The server 104 may include a module (notshown), which enables the server 104 for being introduced to the networkappliance 302, thereby enabling the network appliance 302 to invoke theRCQM device 110-1 as a service. Examples of the network appliance 302include, but are not limited to, a DSL modem, a wireless access point, arouter, a signal repeater or enhancer, and a gateway having apredetermined computing power and memory capacity sufficient forimplementing the RCQM device 110-1. Accordingly, the RCQM devices 110may assist to implement a distributed network architecture, with orwithout the server 104, for executing different aspects of the RCQMdevice 102 of FIG. 1 separately or in tandem through various networkdevices such as the server 104 and the network appliance 302.

Further, in a fourth embodiment (FIG. 4), the RCQM device 102 mayoperate as an independent, standalone device including its ownprocessor(s), such as that shown in FIG. 5, and a transceiver unit (notshown). The RCQM device 102 may be implemented as a single dedicateddevice or that being a combination of multiple dedicated devices.Similar to the user device 108, the standalone RCQM device 102 may beconfigured for being moved or made portable to navigate across one ormore surfaces or regions, which may be spatially apart in the designatedphysical location, or a portion thereof. In some embodiments, suchsurfaces or regions may be separated by a predetermined distance for theRCQM device 102, or a portion thereof, to pass therethrough. The RCQMdevice 102 may accordingly communicate directly with the network devicessuch as the user device 108, the server 104, and the network appliance302 over the network 106 using the transceiver unit. Other embodimentsmay include the RCQM device 102, or aspects thereof, being implementedin a decentralized network architecture.

FIG. 5 illustrates an exemplary remote cleaning quality management(RCQM) device, according to an embodiment of present disclosure. TheRCQM device 102 may be implemented by way of a single device (e.g., acomputing device, a processor, or an electronic storage device) or acombination of multiple devices that may be operatively connected ornetworked together, such as that shown in FIGS. 2-3. The RCQM device 102may be implemented in hardware or a suitable combination of hardware andsoftware. In some embodiments, the RCQM device 102 may be a hardwaredevice including processor(s) 502 executing machine readable programinstructions to (1) communicate synchronously or asynchronously with oneor more software applications, databases, storage devices, or appliancesoperating via same or different communication protocols, formats,database schemas, platforms or any combination thereof, to send andreceive data pertaining to, without limitation, a geographical location;a designated physical location and/or portions thereof, and physical andnon-physical characteristics associated therewith; plot points, clocktimes and durations such as cumulative durations; floor plans; cleaningattributes; cleaning quality; users/owners/caretakers/custodians,objects; and attributes of network devices and signals receivedtherefrom; (2) collect, define, store, and analyze the data duration;(3) formulate one or more tasks for being performed on the data forcreating a training dataset; (4) provide, execute, communicate, andassist in formulating one or more mathematical models for tasks relatedto identification, manipulation, and presentation of the data duration;(5) display, print, or communicate the identified, manipulated, andpresentable data duration; and (6) transfer or map the data includingmodels, tasks, attributes, and attribute values, or any combinationsthereof, to one or more networked computing devices and datarepositories.

The “hardware” may comprise a combination of discrete components, anintegrated circuit, an application-specific integrated circuit, a fieldprogrammable gate array, a digital signal processor, or other suitablehardware. The “software” may comprise one or more objects, agents,threads, lines of code, subroutines, separate software applications, twoor more lines of code or other suitable software structures operating inone or more software applications or on one or more processors. Theprocessors such as the processor(s) 502 may include, for example,microprocessors, microcomputers, microcontrollers, digital signalprocessors, central processing units, state machines, logic circuits,and/or any devices that manipulate signals based on operationalinstructions. Among other capabilities, the processor(s) 502 may beconfigured to fetch and execute machine readable instructions in adedicated or shared memory operatively associated with the RCQM device102 for performing tasks such as signal coding, data processing, I/Oprocessing, power control, and/or other functions.

In some embodiments, the RCQM device 102 may include, wholly or in part,a software application working alone or in conjunction with one or morehardware resources. Such software application may be executed by theprocessor(s) 502 on different hardware platforms or emulated in avirtual environment. Aspects of the RCQM device 102 may leverage known,related art, or later developed off-the-shelf software. Otherembodiments may comprise the RCQM device 102 being in communication witha mobile switching center, network gateway system, Internet access node,application server, IMS core, service node, or any other type ofcommunication systems, including any combinations thereof. In someembodiments, the RCQM device 102 may be integrated with or implementedas a wearable device including, but not limited to, a fashion accessory(e.g., wristbands, rings, pendants, bracelets, etc.), a utility device(e.g., hand-held batons, pens, portable speakers, watches, pen drives,shoes, etc.), a body clothing (e.g., gloves, aprons, jackets, etc.), asafety gear, or any combinations thereof.

The RCQM device 102 also includes a variety of known, related art, orlater developed interface(s) 504 including software interfaces (e.g.,application programming interfaces, graphical user interfaces, etc.);hardware interfaces (e.g., cable connectors, physical or digitalkeyboards, card readers, barcode readers, radio frequency identity(RFID) readers, biometric scanners, interactive display screens,transceiver circuits, etc.); or both. The interface(s) 504 may assistthe RCQM device 102 to communicate with the network devices such as theserver 104.

The RCQM device 102 may further include a memory 506 for storing, atleast one of: (1) files and related data including metadata, e.g., datasize, data format, creation date, associated tags or labels, images,documents, messages or conversations, etc.; (2) a log of profiles ofnetwork devices and associated communications including instructions,queries, conversations, data, and related metadata; and (3) predefinedor dynamically defined, calculated, manipulated, or used mathematicalmodels, equations, or methods for, without limitation, (i) imageprocessing; (ii) mapping or assigning plot points; (iii) signalanalysis; (iv) recording clock times and calculating durations; (v)proximity computations; (vi) creating training datasets; (vii) definingvirtual fences; (viii) remotely assessing the cleaning quality; and soon.

The memory 506 may comprise any computer-readable medium known in theart, related art, or developed later including, for example, a processoror multiple processors operatively connected together, volatile memory(e.g., RAM, etc.), non-volatile memory (e.g., flash, etc.), disk drive,etc., or any combinations thereof. The memory 506 may include one ormore databases such as a database 510, which may be sub-divided intofurther databases for storing electronic files and data. The memory 506may have one of many database schemas known in the art, related art, ordeveloped later for storing the data, predefined or dynamically definedmodels, parameters or attributes, and values thereof. For example, thedatabase 510 may have a relational database schema involving a primarykey attribute and one or more secondary attributes. In some embodiments,the RCQM device 102 may perform one or more operations including, butnot limited to, reading, writing, deleting, indexing, segmenting,labeling, updating, and manipulating the data, or any combinationsthereof, and may communicate the resultant data to various networkedcomputing devices. In one embodiment, the memory 506 may include variousmodules such as an input module 512, a plot training module 514, an RCQMmodule 516, and an output module 518. The operations of these modulesare described below majorly in the context of Wi-Fi signals projectedfrom wireless access points (WAPs) within indoor locations; however, onehaving ordinary skill in the art would understand that the RCQM device102 or any of modules operatively associated therewith may be configuredto operate with any of a variety of types of signals (e.g.,radiofrequency (RF) signals, light signals, sound signals, etc.) orcommunication technology standards (e.g., Li-Fi, Bluetooth®, Zigbee®,etc.) suitable for remote localization at the designated physicallocations, or portions thereof.

Input Module

The input module 512 may communicate with the network devices via theinterface(s) 504 over the network 106. In one embodiment, the inputmodule 512 may implement an exemplary method 600 illustrated in FIG. 6in communication with the network devices. The order in which the method600 is described is not intended to be construed as a limitation, andany number of the described method blocks may be combined, deleted, orotherwise performed in any order to implement the method 600 or analternate method without departing from the scope and spirit of thepresent disclosure. The exemplary method 600 may be described in thegeneral context of computer-executable instructions, which may be storedon a computer readable medium, and installed or embedded in anappropriate device for execution. Further, the method 600 may beimplemented in any suitable hardware, software, firmware, or combinationthereof, that exists in the related art or that is later developed.

At step 602, a floor plan of a designated physical location may bereceived. In one embodiment, the input module 512 may be preconfiguredor dynamically configured to receive one or more inputs including afloor plan from a storage unit such as the storage unit 508 located onthe server 104; however, one of skill in the art would understand thatthe floor plan may be located or accessed on a standalone storage deviceor any other network devices. In some embodiments, the floor plan or aportion thereof may be stored in a local database such as the database510 and fetched by the input module 512 as required. In one example, thefloor plan may be a scale digital diagram of the designated physicallocation such as an arrangement of rooms within a building (e.g.,airport, restaurant, etc.), which may be indicative of a geographicallocation.

Along with the floor plan, the input module 512 may also receive a setof predefined basic and/or extended physical characteristics(hereinafter, collectively referred to as physical characteristics) andnon-physical characteristics of the designated physical location or aportion thereof. In some embodiments, the physical characteristics andthe non-physical characteristics may be associated with the floor plan.Examples of the non-physical characteristics may include, but are notlimited to, a room number, a room name, a floor number, a building name,a department name, or any other aspect that may provide a reference toan intended portion of the designated physical location. Examples of thebasic physical characteristics may include, but are not limited to, (1)dimensions such as length, breadth, height, and area; (2) boundaries, orpartitions therein, such as walls, doors, and windows; and (3) entry andexit points such as doors. Further, examples of the extended physicalcharacteristics may include, but are not limited to, (1) tangibleobjects within the designated physical location such as (i) chattel(e.g., furniture such as chairs, sofas, utility and gaming tables, floorlamps, and garbage bins; kitchen appliances such as cooking ranges andcoffee machines; electronic devices such as television, desktops,electronic worksurfaces, photocopiers, and landline phones; utilityitems such as vending machines, water dispensers, ladders, and vacuumcleaners; decorative pieces such as aquariums, indoor plants, andpaintings; movable carriers for goods and living beings such asmotorized or non-motorized vehicles, wheelchairs, strollers, andcontainers, including parts (e.g., ramps, cushions, tires, etc.) capableof being used with the movable carriers; etc.), (ii) fixtures such aslamp shades, sinks, urinals, cupboards, closets, counters, firehydrants, manholes, etc.), (iii) fittings (e.g., carpets, curtains,blinds, mirrors, faucets, etc.); (2) physical pathways or surfacesadjacent or in-between the tangible objects; (3) spatial arrangementindicators (e.g., number, types, dimensions, and geometries of thetangible objects including any openings therein or parts thereof;relative distances of the tangible objects from each other or fromproximate/neighboring regions such as restrooms, entry and/or exitpoints, stairs, and elevators, or any portions thereof; etc.); and soon. Furthermore, the input module 512 may receive one or more cleaningattributes associated with the designated physical location, or aportion thereof. Examples of the cleaning attributes may include, butare not limited to, (i) a cleaning schedule, (ii) a cleaning task or atype thereof, (iii) a cleaning product, (iv) a cleaning equipment, (v) aproximity of the designated physical location, or a portion thereof,from a user or a predefined area (e.g., restrooms, entry/exit points,etc.) proximate to the designated physical location, (vi) a clock timeor a preset period, or any combinations thereof. Examples of thecleaning product may include any type (e.g., contact-based, contactless,organic, inorganic, etc.) of cleaning, disinfecting, or sterilizingagent known in the art, related art, or developed later includingall-purpose cleaners, dishwashing agents, fabric cleaners or softeners,floor cleaners, ultraviolet light-based surface cleaners, toilet ordrain cleaners, metal cleaners, and so on.

Other embodiments may include the input module 512 additionallydetermining GPS coordinates of a geographical location indicating thedesignated physical location. For example, the input module 512 mayrecord GPS coordinates at various points along a boundary of thegeographical location such that a contour joining these GPS points maydefine a geofence. In one example, such geofence may be defined as animaginary circle having a center corresponding to latitude and longitudecoordinates of a street address of the geographical location such as anairport building, where the imaginary circle may have a radiusequivalent to a set value based on the size of the geographicallocation. These GPS coordinates may be stored in the database 510 or astorage device such as the storage unit 508 and accessed by the inputmodule 512 as required. In one embodiment, the input module 512 may alsodetermine the availability of the RCQM device 102 at the geographicallocation based on a GPS location of the RCQM device 102 being within anarea defined by the geofence (hereinafter referred to as geofence area).The input module 512 may also record the clock times and durations forwhich the RCQM device 102, and/or an associated cleaning entity, may beavailable within the geofence area at the geographical location.

At step 604, the input module 512 may select a region of the floor plancorresponding to a portion of the designated physical location. In oneembodiment, the floor plan may include or indicate an arrangement ofrooms on a storey of a building, in which a region of the floor plan maybe selected. In one example, the region may correspond to a specificroom on that storey where a cleaning task may be performed. The inputmodule 512 may select the region based on the non-physicalcharacteristics of the corresponding designated physical location and/orthe one or more cleaning attributes. The selected region may correspondto a designated cleaning location defined by the associated non-physicalcharacteristics and the cleaning attributes. Some embodiments mayinclude the input module 512 selecting such region of the floor planbased on a user input.

At step 606, the input module 512 may assign a set of preliminary plotpoints in the selected region of the floor plan to generate a plot plan.In one embodiment, the input module 512 may assign the preliminary plotpoints on the selected region based on the one or more physicalcharacteristics of the corresponding designated physical location. Inone instance, the input module 512 may assign the preliminary plotpoints based on boundaries, and/or partitions, of the designatedphysical location indicated in the selected region of the floor plan. Inanother instance, the input module 512 may be configured to identify theboundaries and/or partitions in the selected region using any of avariety of computer vision and machine learning methods known in theart, related art, or developed later based on various aspects including,but not limited to, shape, size, texture, and color or any other aspectsof image objects indicated in the floor plan. Examples of the computervision methods may include, but are not limited to, region-based imagesegmentation methods (e.g., threshold segmentation, region growthsegmentation, etc.); edge detection methods (e.g., Sobel operator,Laplacian operator, etc.); and so on. Examples of the machine learningmethods may include, but are not limited to, supervised learning methods(e.g., Gaussian process regression, Naive Bayes classifier, conditionalrandom field, convolutional neural networks, etc.); unsupervisedlearning methods (e.g., expectation-maximization algorithm, vectorquantization, generative topographic map, information bottleneck method,etc.); and semi-supervised learning methods (e.g., generative models,low-density separation, graph-based methods, heuristic approaches,etc.). In some instances, the boundaries and/or partitions may bepre-marked on the floor plan using tags, or any other metadata, forbeing identified by the input module 512.

Once the boundaries, and/or partitions are identified, the input module512 may assign the preliminary plot points proximate to the boundaries,and/or partitions, indicated in the selected region. In one embodiment,each of the assigned preliminary plot points may have a shortestrelative distance from a specific preliminary point, or any image objector feature, indicated in the selected region. For example, thepreliminary plot points may be placed along the boundaries with eachpreliminary plot point at a shortest relative distance from a corner, ora first preliminary plot point, within the selected region of the floorplan. In some embodiments, the assigned preliminary plot points maysubstantially define the selected region of the floor plan. For example,a set of the assigned preliminary plot points may substantially enclosethe selected region of the floor plan. Similarly, in another example,the preliminary plot points may be placed along one or more partitionsin, or proximate to, a boundary such that each of those preliminary plotpoints may be at a shortest relative distance from a closest preliminaryplot point, which may be located along that boundary of the selectedregion. In some embodiments, the input module 512 may assign thepreliminary plot points at a predefined distance from each other as wellas the boundaries and/or partitions. In one instance, the predefineddistance between any two consecutive preliminary plot points may be sameor different from that between any other two or more consecutivepreliminary plot points. In another instance, such predefined distancemay be zero for the consecutive preliminary plot points being in contactwith each other, thereby creating a continuous trail.

Other embodiments may include these preliminary plot points being,additionally or alternatively, preassigned to the selected region by auser in accordance with the one or more physical characteristics of thedesignated physical location, or the cleaning attributes, or both. Forexample, the preliminary plot points may be preassigned to those areasof the selected region that may represent obscured surfaces, or a partthereof, within the corresponding designated physical location. Suchobscured surfaces may refer to surfaces or regions which may be beyondthe field of view of or a preset distance from a hypothetical user at apredefined or intended location (e.g., a shared pathway) within thedesignated physical location. Examples of the obscured surfaces mayinclude, but are not limited to, an underside of a table, a surfaceunderneath a couch, a top surface of a bookshelf, a constricted areabehind a door, etc. Therefore, the preliminary plot points may representvirtual reference points that may advantageously assist to (i) remotelyidentify optimum portions of a designated physical location on the floorplan where performing a cleaning task may be required to achieve adesired cleaning quality at that designated physical location; (ii)identify physically accessible and/or inaccessible surfaces or regionsof the designated physical location on the floor plan; (iii) improve theaccuracy of mapping or defining the designated physical location on thefloor plan; and (iv) define a virtual fence across the designatedphysical location.

Further, the input module 512 may select more regions, if desired, ofthe floor plan in a manner discussed above to identify or assign thepreliminary plot points and generate a plot plan, which may beindicative of a distribution of the preliminary plot points on the floorplan. Like the floor plan, the plot plan may be produced in a variety offormats including, but not limited to, a two-dimensional (2D) or athree-dimensional (3D) interactive pictorial imagery, a virtual model, amathematical representation, a graph, or any combinations thereofcompatible with other modules and devices. The plot plan, the set ofassigned preliminary plot points, the associated physical andnon-physical characteristics, and the cleaning attributes may becommunicated to the plot training module 514, and/or stored in adatabase such as the database 510 or the storage unit 508, using theinput module 512.

Plot Training Module

The plot training module 514 may operate in communication with variousmodules such as the input module 512, the RCQM module 516, and theoutput module 518, as well as network devices such as the server 104 andthe network appliance 302. However, in some instances, aspects of theplot training module 514 may be implemented on a network device such asthe network appliance 302. In one embodiment, the plot training module514 may implement an exemplary method 700 illustrated in FIG. 7. Theorder in which the method 700 is described is not intended to beconstrued as a limitation, and any number of the described method blocksmay be combined, deleted, or otherwise performed in any order toimplement the method 700 or an alternate method without departing fromthe scope and spirit of the present disclosure. The exemplary method 700may be described in the general context of computer-executableinstructions, which may be stored on a computer readable medium, andinstalled or embedded in an appropriate device for execution. Further,the method 700 may be implemented in any suitable hardware, software,firmware, or combination thereof, that exists in the related art or thatis later developed.

At step 702, a plot plan and a set of preliminary plot points associatedtherewith may be accessed. In one embodiment, the plot training module514 may receive the plot plan and the associated set of preliminary plotpoints from the input module 512. However, in some embodiments, the plottraining module 514 may access the plot plan or the preliminary plotpoints from the database 510 or the storage unit 508 on the server 104over the network 106. Other embodiments may include the plot trainingmodule 514 accessing the plot plan or the set of preliminary plot pointsfrom a standalone storage device or any networked devices.

At step 704, a predefined signal received from at least one fixednetwork device may be scanned. The plot training module 514 may beconfigured to scan for signals received from at least one spatiallyfixed network device (or simply, fixed network device) when the RCQMdevice 102 may be proximate to the designated physical locationrepresented by the plot plan. The fixed network device may refer to anynetwork device or appliance that may be fixed, or movably fixed, to apredefined physical region and configured to establish a network (e.g.,network 106) with the RCQM device 102 being proximate to the designatedphysical location. One of skill in the art would understand thatportable network devices (e.g., RF beacons, mobile phones, etc.) capableof establishing a network with the RCQM device 102 may also be spatiallyfixated for the purposes of being used as the fixed network device.

Further, the proximity to the designated physical location may bedetermined based on (i) a user input, (ii) detectable signals receivedfrom the fixed network device, or (ii) a trigger provided by anysuitable sensor associated with either the designated physical locationor the RCQM device 102. The sensor may be a signal sensor, or aproximity sensor, configured for triggering the plot training module 514to initiate the signal scanning based on the RCQM device 102 beingproximate to the designated physical location. In some instances, theplot training module 514 may additionally or alternatively define ascanning proximity based on a variety of factors including, but notlimited to, the number and types of available network devices and thesignals received therefrom, the strength of received signals relative toa predefined signal threshold value, and computational delays. Forexample, the plot training module 514, or the RCQM device 102, may beconfigured to increase the scanning proximity if (1) only one networkdevice such as an access point is detected to be available; (2) thestrength of signals received from a predefined number of network devicesis either below the predefined signal threshold value or are not stablefor a predetermined amount of time; (3) more than half of the predefinednumber of network devices are repeaters or mobile hotspots; or (4) atleast one fixed network device providing stable signals is unavailable;and so on. In another example, the plot training module 514 may decreasethe scanning proximity if the number of available network devicesadversely affect the intended computational complexity, accuracy, orcause delay.

In one embodiment, the plot training module 514 may be preconfigured ordynamically configured to scan for a predefined signal received from atleast one fixed network device. For example, the plot training module514 may scan for radiofrequency signals such as Wi-Fi signals from anetwork device such as a fixed wireless access point. Further, the Wi-Fisignals may comprise of one or more signal samples, each being in theform of packets. Each signal sample may be associated with a networkdevice identifier (ID) such as a basic service set identifier (BSSID)value indicative of the media access control (MAC) address of a wirelessaccess point, e.g., the network appliance 302, which generated thatsignal sample.

Another example may include the plot training module 514 beingconfigured to scan for light signals from at least one fixed networkdevice including a light source such as a pulsed light source, acontinuous light source, or a set of both the pulsed and the continuouslight sources. The pulsed light source may be configured by a controldevice (not shown) to emit pulses of light of a predetermined energyintensity, power, or dose within a predefined or dynamically definedwavelength range. In some embodiments, the pulsed light source may beconfigured by the control device to have a pulse frequency and/or pulseduration that may cause the emitted pulsed light to appear as continuousto a human eye. On the other hand, the continuous light source may beconfigured by the control device to emit a continuous stream of light.In some embodiments, the continuous light source may be turned on andoff at a predetermined frequency by the control device to emit pulses oflight. Any of such light sources may be designed as a bulb, a lightemitting diode (LED), a gas discharge lamp, or any other types known inthe art, related art, or developed later, or any combination thereof.The plot training module 514 may be configured with any suitablesoftware and hardware for receiving the light-conveyed information basedon light characteristics (e.g., wavelength, intensity, power, dose,pulse frequency, pulse width, etc.). For example, the plot trainingmodule 514 may operate in communication with a silicon photodiode forreceiving a pulsed visible light signal from a bulb based on the IEEE802.15.7 communication standard. One of skill in the art wouldunderstand that any other suitable types of light detectors known in theart, related art, or developed later including photoconductors(photoresistors), photovoltaic devices (photocells), phototransistors,and photodiodes may be used. Each pulse of the pulsed visible lightsignal may include or indicate, without limitation, device ID of thelight source and characteristics of the light signals receivedtherefrom.

Yet another example may include the plot training module 514 beingconfigured to scan for sound signals from at least one fixed networkdevice including a sound source. The sound signals may include, but arenot limited to, audible signals, inaudible sound signals, vibrationalwaves (e.g., longitudinal waves, transverse waves, surface waves, etc.),electromagnetic hum, sound patterns (e.g., alliteration, assonance,onomatopoeia, rhythmic, non-rhythmic, etc.), background noises, or anycombinations thereof. Examples of the sound source may include, but arenot limited to, sound-based access points, speakers, low-voltage andbattery-operated sound devices capable of producing a sound (e.g.,mobile phones, beacons, sound beepers, etc.), electronic devices (e.g.,television, ovens, refrigerators, printers, vacuum cleaners, servers,ultraviolet (UV) disinfection devices, etc.), powered electroniccomponents (e.g., wires, capacitors, regulators, bulbs, etc.), livingorganisms or any other natural sound sources such as flowing water andwind, or any combinations thereof. The plot training module 514 may beconfigured with any suitable software and hardware for receiving anyinformation conveyed by a sound signal based on its soundcharacteristics (e.g., wavelength, amplitude, time-period, frequency,velocity or speed, pattern, harmony, timbre, etc.). For example, theplot training module 514 may operate in communication with a microphonefor receiving pulses of a sound signal or sound signals received atcertain intervals. Each pulse or stream of the sound signals may includeor indicate, without limitation, device ID of the sound source and thesound characteristics of signals received therefrom.

At step 706, at least one fixed network device may be identified asavailable based on the scanned signal being a stable signal. In oneembodiment, the plot training module 514 may be preconfigured ordynamically configured to determine the scanned signal as being a stablesignal if a predefined number of signal samples or pulses are receivedfor a predetermined amount of time from at least one fixed networkdevice, provided the strengths of signals, or samples thereof, are abovea predefined signal threshold value. For example, the plot trainingmodule 514 may receive RF signals such as Wi-Fi signals from a fixedwireless access point (WAP). Each pulse or a stream of the Wi-Fi signalsmay be received as a signal sample from the fixed WAP. The receivedWi-Fi signal samples may include a received-signal-strength-indication(RSSI) value indicative of its signal strength. If the RSSI value of apredefined number of signal samples (e.g., at least two samples) may beabove a predefined signal threshold value (e.g., −70 decibel-milliwatt(dBm)) for a predefined signal duration (e.g., one millisecond), theplot training module 514 may determine the received Wi-Fi signal as astable signal. Accordingly, the fixed network device such as the fixedWAP (e.g., network appliance 302) generating the stable signal may beidentified as being available. The predefined signal threshold value mayhave any value ranging from −70 dBm to −10 dBm. However, one of skill inthe art would understand that the RSSI value and the predefined signalthreshold value may be equivalently represented in any other suitablemeasurement units depending on the type of network devices providing thesignals. The predefined signal duration value may range fromapproximately one picosecond to approximately one second.

At step 708, one or more signal plot points may be mapped on the plotplan. In one embodiment, the plot training module 514 may map or assignone or more signal plot points on the plot plan based on the receivedstable signal. Each signal plot point may be indicative of a physicalspot in the designated physical location where the stable signal isreceived. Further, the plot training module 514 may map the signal plotpoints relative to the preliminary plot points. For example, the plottraining module 514 may assign a signal plot point proximate to apreliminary plot point on the plot plan if the stable signal is receivedat a physical spot, which corresponds to the preliminary plot point ormay be located within a predefined distance (e.g., at leastapproximately one foot or approximately 0.3 meters) therefrom. Inanother example, the plot training module 514 may assign the signal plotpoint proximate to a preliminary plot point if a stable signal is notreceived at a physical location corresponding to that preliminary plotpoint. Accordingly, the plot training module 514 may assign a signalplot point for another physical spot, which may be within a predefineddistance (e.g., at least approximately one foot or approximately 0.3meters) from the physical location corresponding to the preliminary plotpoint, provided the stable signal is received at that another physicalspot. Such two-step placement of the signal plot points based on averification of (i) stable signals being received at a physical spot,and (ii) proximity to a preliminary plot point indicating anotherphysical spot where the stable signals are received, may assist toidentify physical spots within the designated physical location that maybe easy to track, relevant, accessible, or inaccessible for beingcleaned. Accordingly, in one instance, the signal plot points may bemapped proximate to the preliminary plot points assigned along aboundary of the designated physical location or a portion thereof suchas the designated cleaning location. These signal plot points proximateto the boundary may define an indoor virtual fence substantiallyenclosing the designated physical location, or a portion thereof.Further, the plot training module 514 may assign additional signal plotpoints within a predefined distance (e.g., at least approximately 0.5meters) from a corresponding closest signal plot points, which may beassigned relative to the preliminary plot points. In some embodiments,these additional signal plot points may be assigned at a same ordifferent predefined distance from each other. Other embodiments mayinclude the plot training module 514 continue assigning the signal plotpoints on the plot plan until a physical space indicated by the assignedsignal plot points covers a substantial portion or a selected portion ofthe designated physical location.

In some embodiments, a distance from an assigned signal plot point to(i) another signal plot point, (ii) a preliminary plot point, or (iii) aphysical characteristic such as a boundary indicated on the plot planmay be measured based on a corresponding distance between theirrespective physical spots within the designated physical location. Suchdistance, with or without direction, may be measured using any of avariety of physical methods and/or computer vision methods known in theart, related art, or developed later. Examples of the computer visionmethods may include any of the existing or future methods such as thosementioned above. The computer vision methods may be implemented with anysuitable hardware such as a camera, a light-based circuitry (e.g.,infrared sensor circuitry), and a sound-based circuitry (e.g.,ultrasonic sensor circuitry, altimeter, etc.) for measuring thedistance. Examples of the physical methods may include, but not limitedto, step count or step length measurement (e.g., using a pedometer),assessment of relative change in magnetic field (e.g., using amagnetometer), and so on. In some embodiments, these physical methodsand/or computer vision methods in combination with any suitable hardwaremay also assist in defining indoor location coordinates for the signalplot points and/or the cleaning entity.

When such signal plot points are assigned, the plot training module 514may record an identifier (e.g., BSSID value) of the network device(e.g., WAP) from which the stable signals may be received at thephysical spots corresponding to the assigned signal plot points.Similarly, the plot training module 514 may update the plot plan toindicate one or more signal plot points therein. In one embodiment, theplot training module 514 may be configured to append the signal plotpoints on the plot plan without removing the existing preliminary plotpoints. However, in some embodiments, the plot training module 514 mayremove the preliminary plot points after the signal plot points aremapped or assigned. Therefore, the signal plot points may representvirtual reference points that may advantageously assist to (i) reducehardware costs vis-à-vis powered or active beacons, (ii) remotelyidentify physical spots of interest at an indoor location independent ofthe geographical location and related aspects such as GPS coordinates,(iii) avoid being defined based on plot point clustering or signalbroadcasts from the physical spots of interest, and (iv) define anindoor virtual fence proximate to the physical spots of interest withinthe designated physical location.

At step 710, a training dataset may be created. In one embodiment, theplot training module 514 may create a training dataset based on (i) themapped one or more signal plot points, (ii) the stable signals receivedat the mapped one or more signal plot points, and (iii) the at least onefixed network device providing the stable signal. For example, thetraining dataset may include an entry having a plot point identifier(e.g., a reference number, an indoor location coordinates, etc.) of asignal plot point, a signal identifier such as the strength (e.g., RSSIvalue) of the stable signal received at a physical spot indicated by thesignal plot point, and a network device identifier (e.g., BSSID value)indicating a network device providing the stable signal. In someembodiments, the entry may also include geographical locationcoordinates associated with the designated physical location for whichthe training dataset may be created. The training dataset and the plotplan appended with the assigned signal plot points, hereinafter referredto as a signal plot plan, may be communicated to the RCQM module 516and/or stored in the database such as the database 510 or a storagedevice such as the storage unit 508 using the plot training module 514.

RCQM Module

The RCQM module 516 may communicate with various modules such as theinput module 512, the plot training module 514, and the output module518, as well as network devices such as the server 104 and the networkappliance 302. In one embodiment, among various functionalities, theRCQM module 516 may be preconfigured or dynamically configured to, atleast one of, (1) identify a position of the RCQM device 102 at adesignated physical location relative to a set of selected signal plotpoints; (2) determine an amount of time spent at the identified positionbased on a predefined cleaning schedule; (3) determine a cleaningquality for the designated physical location or a portion thereof basedon the determined amount of time spent at the identified position; and(4) communicate the determined amount of time, and the cleaning qualityto the output module 518. However, aspects of the RCQM module 516 mayalso be implemented on a network device such as the server 104.

In one embodiment, the RCQM module 516 may implement an exemplary method800 illustrated in FIG. 8 according to an embodiment of the presentdisclosure. The order in which the method 800 is described is notintended to be construed as a limitation, and any number of thedescribed method blocks may be combined, deleted, or otherwise performedin any order to implement the method 800 or an alternate method withoutdeparting from the scope and spirit of the present disclosure. Theexemplary method 800 may be described in the general context ofcomputer-executable instructions, which may be stored on a computerreadable medium, and installed or embedded in an appropriate device forexecution. Further, the method 800 may be implemented in any suitablehardware, software, firmware, or combination thereof, that exists in therelated art or that is later developed.

At step 802, a training dataset including a plurality of signal plotpoints and one or more signal strengths associated therewith of apredefined signal may be accessed. In one embodiment, the RCQM module516 may access the training dataset from the database 510 or a storagedevice such as the storage unit 508; however, the training dataset maybe received on-the-fly from the input module 512 or the plot trainingmodule 514. The training dataset may include a set of parameterscorresponding to discrete positions in a designated physical location.For example, the training dataset may include one or more signal plotpoints, each being indicated by a plot point identifier (e.g., numericor alphanumeric values, special characters, binary or hexadecimal codes,etc.). Each of the signal plot points may be assigned on a signal plotplan and correspond to a physical spot at a designated physicallocation, or a portion thereof. The training dataset may also include aset of signal identifiers such as signal strengths (e.g., RSSI values),of a predefined signal associated with at least one of the signal plotpoints. The predefined signal may include a radiofrequency signal, alight signal, a sound signal, or any combinations thereof. For example,the predefined signal may be a Wi-Fi signal. Further, the trainingdataset may include a network device identifier (e.g., BSSID values)indicating at least one fixed network device providing stable signals(e.g., stable Wi-Fi signals) received at the one or more signal plotpoints.

Similarly, the training dataset may be accessed by the server 104 forperforming one or operations in communication with the RCQM device 102.One of skill in the art would understand that aspects of the server 104described in the present disclosure may be performed using the RCQMdevice 102, or any modules thereof in communication, wholly or in-part,with or without the server 104. For example, all aspects of the server104 may be performed or implemented independently using the RCQM device102 or any module operatively associated therewith. In one embodiment,the server 104 may select at least one signal plot point based on one ormore cleaning attributes associated with a designated cleaning locationin the designated physical location. For example, the server 104 mayselect a signal plot point corresponding to a physical spot at thedesignated cleaning location, where the physical spot may be proximateto predefined areas of interest such as entry and exit points. Inanother example, the server 104 may select a signal plot pointcorresponding to a physical spot being proximate to a predefined objectsuch as a television to be cleaned at the designated cleaning location.In yet another example, the server 104 may select a signal plot pointcorresponding to a physical spot in accordance with an associatedcleaning schedule. In still another example, the server 104 may select asignal plot point corresponding to a physical spot being assigned orassociated with a cleaning entity such as a cleaning staff and acleaning equipment, or just any device in general. In a further example,the server 104 may select a signal plot point corresponding to aphysical spot indicated or designated as a high-activity zone. Inanother example, the server 104 may select a signal plot pointcorresponding to a physical spot required to be cleaned continuously fora specific duration or at dynamically set clock times. Other examplesmay include a signal plot point being selected based on a user input.The selected plot point, and aspects thereof, may be stored in thestorage unit 508 or communicated to the RCQM module 516 by the server104. The selected plot point may assist in ensuring that an intendedportion of the indoor location may be attended by the RCQM device 102operatively associated with a cleaning entity.

At step 804, a predefined signal may be received at a position in thedesignated physical location from the at least one fixed network device.In one embodiment, the RCQM module 516 in communication with suitablehardware may scan a predefined signal received from at least one fixednetwork device. The predefined signal as well as the at least one fixednetwork device may be one of those used to create the training dataset.For example, the RCQM module 516 in communication with a suitablehardware (e.g., an RF detector) may be configured to scan for Wi-Fisignals from the network appliance 302. However, one of skill in the artwould understand that other suitable hardware known in the art, relatedart, or developed later may be used depending on the type of signalbeing received.

Further, similar to the step 706 of FIG. 7 discussed above, the RCQMmodule 516 may be configured to determine the received predefined signalas a stable signal if a predefined number of signal samples or pulsesmay be received for a predetermined amount of time from the fixednetwork device, provided the signal strength of each of those samples orpulses may be above a predefined signal threshold value. For example,the RCQM module 516 in communication with an RF detector may detectWi-Fi signals received from the fixed WAP. Each sample of the Wi-Fisignals may include an RSSI value indicative of its signal strength. Inone embodiment, the RCQM module 516 may he configured to determine thereceived Wi-Fi signal as a stable Wi-Fi signal if each of a predefinednumber of Wi-Fi signal samples (e.g., at least two samples) may have anRSSI value above a predefined signal threshold value (e.g., −70 dBm) fora predetermined amount of time (e.g., one millisecond). Examples of thepredefined signal threshold value may include any value being −70 dBm orabove, e.g., −70 dBm, −67 dBm, −60 dBm, −50 dBm, −20 dBm, −10 dBm, etc.based on (i) a type of received predefined signal, and/or (ii) a signalreliability required depending on a distance between the network deviceand the RCQM device 102. In some embodiments, larger the distance,greater may be the signal loss, thereby requiring a relatively highersignal threshold value. Upon determining the received predefined signalsuch as the Wi-Fi signal as the stable signal, the RCQM module 516 mayrecord a second signal strength (e.g., an RSSI value) of the receivedpredefined signal for being used to determine a relative position of theRCQM device 102 at the designated cleaning location. The recorded RSSIvalue or the second signal strength may be stored in the database 510,or the storage unit 508, or communicated directly to the server 104using the RCQM module 516.

The server 104 may receive the second signal strength from the RCQMmodule 516, the database 510, or the storage unit 508. The server 104may use the determined signal identifier such as the second signalstrength in combination with each of a set of first signal strengths inthe received training dataset to determine the relative position of theRCQM device 102 using any of a variety of supervised, semi-supervised,or unsupervised learning methods known in the art, related art, ordeveloped later including, but not limited to, random forest identifier,and so on. In one embodiment, the server 104 may be configured to employthe K-nearest neighbor (KNN) method to determine the relative positionof the RCQM device 102. According to the KNN method, the server 104 maycompute and compare Euclidean distances between the second signalstrength (e.g., a received RSSI value of the stable signal received fromthe fixed wireless access point) and each of the first signal strengths(e.g., the RSSI values, stored in the training dataset). For example, aEuclidean distance D_(i) may be computed based on equation 1:

D _(i)=√{square root over (Σ_(j=1) ^(n)(RSSI_(ij)−RSSI_(j))²)}  (1)

where:

-   D_(i)=Euclidean distance-   RSSI_(ij)=RSSI values stored in the training dataset;-   1≤i≤no. of RSSI values in training dataset-   RSSI_(j)=RSSI value received from a network device;-   1≤j≤no. of network devices

Based on the comparison, the server 104 may determine a stored RSSIvalue in the training dataset for which the calculated Euclideandistance D_(i) with the received RSSI value is the shortest. This storedRSSI value providing the shortest Euclidean distance D_(i) may assist todetermine the position of the RCQM device 102 relative to a signal plotpoint, which may correspond to a physical spot at the designatedcleaning location. The calculated shortest Euclidean distance may becompared with a predefined Euclidean distance threshold value(hereinafter referred to as distance threshold value), which may definea proximity from the signal plot point. The server 104 may determine theRCQM device 102 being proximate to the signal plot point based on thecorresponding calculated shortest Euclidean distance being less than thepredefined distance threshold value. Accordingly, a signal plot pointfrom the training dataset whose associated stored RSSI value providesthe shortest Euclidean distance relative to the predefined distancethreshold value, such signal plot point may be considered nearest to theRCQM device 102. The predefined distance threshold value may have asuitable Euclidean distance value (in dBm) corresponding to a metricdistance value ranging from approximately 0.3 meters to approximately 2meters. One of skill in the art may employ any suitable mathematicalmethods known in the art, related art, or developed later to representthe Euclidean distance value (in dBM) of the predefined distancethreshold value into any suitable distance measuring unit, or viceversa, for being comparable with the calculated Euclidean distances.Accordingly, the server 104 may determine the position of the RCQMdevice 102 being proximate to the a signal plot point (hereinafterreferred to as base signal plot point) based on a signal strength (e.g.,a first RSSI value) from the set of first signal strengths in thetraining dataset that provides the shortest Euclidean distance with thesecond signal strength (e.g., a second RSSI value) of the receivedsignal. Additionally, or alternatively, camera images corresponding tothe signal plot points and/or orientation or direction sensor data ofthe RCQM device 102 may be used to further assist in determining theposition of the RCQM device 102 relative to a signal plot point such atthe designated cleaning location.

Other embodiments may include the server 104 being preconfigured ordynamically configured to determine at least two stored RSSI values andassociated signal plot points, which may be nearest to the RCQM device102 for estimating a relative position of the RCQM device 102. Forexample, the server 104 may determine a set of at least two nearestsignal plot points between which the RCQM device 102 may be locatedbased on the shortest Euclidean distances between the RSSI value of thereceived signal and those RSSI values stored in the training dataset asdiscussed above. The server 104 may determine these two stored RSSIvalues based on the corresponding Euclidean distances being the shortestwith the received RSSI value (e.g., the second RSSI value) relative toother RSSI values stored in the training dataset. In some instances, theEuclidean distances of the second RSSI value with each of these twostored RSSI values may be different. Yet another embodiment may includethe server 104 being preconfigured or dynamically configured todetermine at least three signal plot points nearest to the RCQM device,such that the RCQM device 102 may be located in-between the at least twoof those three signal plot points, which may be non-linearly arranged onthe signal plot plan. The server 104 may determine a region defined bysuch non-linear signal plot points on the signal plot plan for thedesignated cleaning location. Further, the server 104 may fetch theselected plot point, and aspects thereof, from the storage unit 508 forbeing compared with the base signal plot point. If there is match basedon the comparison, the server 104 may determine the base signal plotpoint as being the selected plot point and the position of the RCQMdevice 102 being proximate thereto. The server 104 may accordinglycommunicate the determined position of the RCQM device 102 as beingproximate to the selected plot point to the RCQM module 516.

At step 806, a cumulative duration spent at the determined position maybe calculated. The RCQM module 516 may be preconfigured or dynamicallyconfigured to determine an amount of time spent by the RCQM device 102at the position determined as being proximate to a signal plot pointsuch as the base signal plot point upon being determined as the selectedsignal plot point. In one embodiment, the RCQM module 516 may determinea cumulative duration spent at the position based on a predefinedcleaning schedule associated with the designated cleaning locationincluding the determined position. In some embodiments, the cleaningschedule may be associated with the physical spot corresponding to asignal plot point such as the selected signal plot point. For instance,the RCQM module 516, or the server 104, may define a cleaning scheduleas a set of a cleaning task such as wiping dust and stains, and amaximum duration such as 300 seconds associated therewith for completingthat cleaning task within a preset period such as 8 hours between clocktimes 9:00 am and 5:00 pm. The maximum duration may refer to a maximumamount of time available or set for completing an intended cleaning taskor a set of cleaning tasks at the determined position proximate to asignal plot point such as the selected signal plot point. The value ofmaximum duration may be defined or adjusted based on (i) the cleaningtask and/or (ii) the designated physical location or a portion thereofassociated with the cleaning schedule. In some embodiments, the maximumduration may be less than or equal to the preset period. The presetperiod may refer to a predefined duration for which a cleaning entitymay be required to be available at the designated physical location, ora portion thereof. In some embodiments, the preset period may correspondto an assigned work shift and defined as a duration between a presetentry clock time and a preset exit clock time for the cleaning entity atthe designated physical location or a portion thereof such as thedesignated cleaning location. Other embodiments may include the presetperiod being defined as a duration between a preset entry clock time anda preset exit clock time for the cleaning entity at a geographicallocation indicating the designated physical location, or a portionthereof.

The RCQM module 516 may be configured to record the cumulative durationspent at the determined position anytime within the preset period, forexample, of 8 hours, irrespective of the RCQM device 102 moving awayfrom the selected signal plot point. As such, the cumulative durationmay refer to a sum of temporally separate discrete durations spent atthe determined position within the preset period. The RCQM module 516,in communication with the server 104, may determine the RCQM device 102being moved from the determined position to a new location away from theselected signal plot point proximate to the determined position based onthe shortest Euclidean distance calculated previously between thecorresponding first signal strength (e.g., RSSI value) stored in thetraining dataset and the received second signal strength at thatposition. If this shortest Euclidean distance exceeds the predefineddistance threshold value, the RCQM module 516 may determine the RCQMdevice 102 being moved outside the predefined proximity of the selectedsignal plot point.

The RCQM module 516 may record the duration for each time the positionof the RCQM device 102 may be determined proximate to the selectedsignal plot point within the preset period of the cleaning schedule.Accordingly, the RCQM module 516 may add the recorded durations atdifferent intervals or clock times within the preset period of thecleaning schedule to determine the cumulative duration spent at thedetermined position proximate to a signal plot point, such as theselected plot point, which may be chosen based on the one or morecleaning attributes, as discussed above. However, in some embodiments,the cumulative duration may refer to a single continuous duration spentat the determined position while the RCQM device 102 remains proximateto a signal plot point such as the selected signal plot point. In someinstances, such continuous duration may assist to qualify physicalinspections of the determined position or a physical spot correspondingto the proximate signal plot point such as the selected signal plotpoint. For example, the continuous duration spent at the determinedposition proximate to a signal plot point relative to a preset timethreshold value may indicate a predefined cleaning quality or a qualityof inspection performed by an inspection staff. The calculate cumulativeduration may be communicated to the server 104, or stored in thedatabase 510 or the storage unit 508 by the RCQM module 516.

In one embodiment, the server 104 may be preconfigured or dynamicallyconfigured to assess a cleaning quality for the determined positionbased on the calculated cumulative duration relative to a set of one ormore predefined time threshold values. For example, as illustrated inFIG. 9, the server 104 may predefine or dynamically define a metric 900involving a set of predefined time threshold values such as a first timethreshold value 902-1, a second time threshold value 902-2, and a thirdtime threshold value 902-3, collectively referred to as predefined timethreshold values 902. The cumulative duration up to a maximum timethreshold value such as the third time threshold value 902-3 may referto an accounted duration, which may be used by the server 104 to assessthe cleaning quality. The accounted duration may indicate an acceptabletime period for performing a cleaning-related actions (e.g., cleaningtasks, inspections, etc.) that may be set based on a user input ortime-bound cleaning obligations. However, a portion of the cumulativeduration exceeding the maximum time threshold value may refer to anunaccounted duration, which may not be used by the server 104 to assessthe cleaning quality. The unaccounted duration may indicate a timeperiod exceeding the time-bound cleaning obligations or a user input.For example, the unaccounted duration may indicate inefficiencies inperforming a cleaning task or a break time. Another example may includethe unaccounted duration being indicative of non-chargeable or unpaidhours in a scenario of delivering cleaning services.

In one instance, the RCQM module 516 may assess the cleaning quality forthe determined position as (1) “Low quality” if the cumulative durationspent by the RCQM device 102 may be less than or equal to the first timethreshold value 902-1; (2) “Average quality” if the cumulative durationspent is below or equal to the second time threshold value 902-2 andabove the first time threshold value 902-1; or (3) “High quality” if thecumulative duration is less than or equal to the third time thresholdvalue 902-3 and above the second time threshold value 902-2. In otherinstances where the cumulative duration may be above the third timethreshold value 902-3, the server 104 may consider a portion of thecumulative duration exceeding the third time threshold value 902-3 asthe unaccounted duration, which may be indicative of a recess/break timeor inefficient cleaning performance. The server 104 may provide anindication (e.g., audio, visual, haptic, text-based, symbolic, or anycombinations thereof) based on the calculated cumulative durationexceeding the maximum time threshold value such as the third timethreshold value 902-3. In some embodiments, the cleaning quality may befurther assessed based on (1) a number of passes made by the RCQM device102 proximate to a signal plot point preselected based on the one ormore cleaning attributes associated with the designated cleaninglocation, (2) relative proximity of the RCQM device 102 to the physicalspots corresponding to the one or more selected signal plot points, or(3) such relative proximity while the RCQM device 102 passes along adifferent physical spot corresponding to another signal plot point.

In one embodiment, the predefined time threshold values 902 may berelative to a total time spent proximate to at least one of (i) aphysical spot corresponding to a signal plot point, (ii) the designatedphysical location or a portion thereof, and (iii) a geographicallocation indicating the designated physical location, or a portionthereof, and any combinations thereof. For example, the first timethreshold value 902-1 may be 30 seconds, the second time threshold value902-2 may be 60 seconds, and the third time threshold value 902-3 may be120 seconds spent proximate to the physical spot corresponding to thesignal plot point such as the selected signal plot point. In someembodiments, at least one of the predefined time threshold values 902may be equivalent to at least one maximum duration noted in thepredefined cleaning schedule. For example, the maximum time thresholdvalue (e.g., the third time threshold value 902-3) may be equivalent toat least one maximum duration noted in the predefined cleaning schedule.The first time threshold value 902-1 and the second time threshold value902-2 may be defined or adjusted based on a variety of factorsincluding, but not limited to, (1) the experience, skill, trainings, orpast cleaning performances of a cleaning entity; (2) a starting cleaningcondition or cleanliness of the designated physical location or that ofthe designated cleaning location; (3) intended use or purpose of thedesignated cleaning location or associated designated physical location;(4) defined time-bound cleaning obligations, e.g., in a cleaning servicecontract; (5) a request or feedback from a user; (6) physicalcharacteristics of the designated cleaning location such as thosementioned above including size, number of objects, and room type such asa conference room or a bathroom; (7) a clock time of the next intendeduse of the designated cleaning location or the next cleaning orinspection tasks; (8) a type of cleaning task (e.g., simple dust removalversus removal of hard stains); (9) number of inspections or outcomesthereof; (10) use of different cleaning technologies or equipment; orany combinations thereof.

In another embodiment, the server 104 may include a predefined timethreshold value with respect to a specific unit of area. For instance,the RCQM module 516 may predefine or dynamically define a time thresholdvalue per square meter to assess the cleaning quality. Based on aposition of the RCQM device 102 between the nearest at least two signalplot points, the RCQM device 102 may determine the cleaning quality as(1) “Low quality” if a cumulative duration spent by the RCQM device 102within at least one square meter of a region between the nearest atleast two signal plot points may be less than or equal to a firstpredefined time threshold value per square meter (e.g., 60 seconds); (2)“Average quality” if that cumulative duration spent within the at leastone square meter of the region is greater than the first predefined timethreshold value per square meter but less than or equal to a secondpredefined time threshold value per square meter (e.g., 120 seconds); or(3) “High quality” if that cumulative duration spent within the at leastone square meter of the region is greater than the second predefinedtime threshold value per square meter but less than or equal to a thirdpredefined time threshold value per square meter (e.g., 240 seconds).

In yet another embodiment, the server 104 may also calculate a totaltime spent by the RCQM device 102 at a geographical location indicatingthe designated physical location. Such total time may be calculatedbased on an entry clock time and an exit clock time of the RCQM device102 at the geographical location associated therewith. Accordingly, theserver 104 may assess the cleaning quality based on the total time spentby the RCQM device 102 at various designated cleaning locations orphysical spots indicated by the signal plot points within thegeographical location. For instance, the server 104 may assess thecleaning quality as (1) “Unacceptable” if the cumulative duration spentby the RCQM device 102 proximate to a signal plot point selected basedon a cleaning attribute may be less than or equal to 20% of the totaltime spent at the geographical location indicating the designatedphysical location; (2) as “Need improvement” if that cumulative durationspent may be greater than 20% but less than or equal to 60% of the totaltime spent at the geographical location; and (3) as “Acceptable” if thatcumulative duration spent may be greater than 60% of the total timespent at the geographical location.

In further embodiments, the server 104 may accordingly highlight a setof signal plot points on the signal plot plan based on the cleaningquality assessed relative to the set of predefined time threshold values902. These highlighted set of signal plot points may be different fromthose that were selected earlier by the server 104 based on the cleaningattribute. The highlighted signal plot points may be proximate to theRCQM device 102 or through which the RCQM device 102 may have passed atthe designated cleaning location or the designated physical location.

In yet another embodiment, the server 104 may be preconfigured ordynamically configured to compute various aspects related to thecleaning entity based on the calculated durations including thecumulative duration and the assessed cleaning quality. For example, theserver 104 may compute a cleaning performance of the cleaning entity asshown in equation 2:

$\begin{matrix}{{{Cleaning}\mspace{14mu} {Performance}} = \frac{{Cumulative}\mspace{14mu} {Duration}_{Accounted}}{{Total}\mspace{14mu} {Cumulative}\mspace{14mu} {Duration}_{{Accounted} + {Unaccounted}}}} & (2)\end{matrix}$

where:

-   Cumulative Duration_(Accounted)=a portion of a cumulative duration    spent at a physical location up to a predefined maximum time    threshold value-   Total Cumulative Duration_(Accounted+Unaccounted)=the total    cumulative duration spent at the physical location irrespective of    the predefined maximum time threshold value

As shown in equation 2, the cleaning performance may be calculated as aratio of the cumulative duration spent at a physical location, such asthe determined position, up to a predefined maximum time threshold valueand a total cumulative duration spent at that physical locationirrespective of the predefined maximum time threshold value. In oneexample, the predefined maximum time threshold value may be a maximumduration available or set for performing an intended cleaning task or aset of cleaning tasks at the determined position proximate to a signalplot point such as the selected plot point. Both the cumulative durationand the total cumulative duration may be represented in seconds;however, other suitable measurement or referential units may becontemplated. Further, the server 104 may be configured to compute atime efficiency of the cleaning entity based on the calculatedcumulative duration, the calculated unaccounted duration, and the presetperiod based on equation 3:

$\begin{matrix}{{{Time}\mspace{14mu} {Efficiency}} = {\frac{{Cumulative}\mspace{14mu} {Duration}_{Accounted}}{{Preset}\mspace{14mu} {Period}} \times 100}} & (3)\end{matrix}$

where:

-   Cumulative Duration_(Accounted)=a portion of a cumulative duration    spent at a physical location up to a predefined maximum time    threshold value-   Preset Period=a predefined duration for which a cleaning entity is    required to be available at the physical location as per the    predefined cleaning schedule

In equation 3, the cumulative duration and the preset period may berepresented in seconds, and the time efficiency may be represented inpercentage; however, other suitable units or referential values may becontemplated. The calculated time efficiency and/or the cleaningperformance may be stored in a storage device such as the storage unit508. Embodiments may further include the server 104 being preconfiguredor dynamically configured to (i) compute a performance rating of acleaning entity, (ii) compute or improve cleaning-related billing,and/or (iii) conduct remote cleaning inspections for the determinedposition proximate to the selected plot point based on the calculatedtime efficiency and/or the cleaning performance of the cleaning entity.

In one embodiment, the server 104 may be preconfigured or dynamicallyconfigured to determine a performance rating of a cleaning entity basedon the calculated cleaning performance and/or the time efficiency. Forexample, the server 104 may assign a rating as (i) “Excellent” if thecalculated time efficiency may be greater than or equal to 90%, or thecalculated cleaning performance may be greater than or equal to 0.9;(ii) “Good” if the calculated time efficiency may be greater than orequal to 80% but less than 90%, or the calculated cleaning performancemay be greater than or equal to 0.8 but less than 0.9; (iii) “Average”if the calculated time efficiency may be greater than or equal to 70%but less than 80%, or the calculated cleaning performance may be greaterthan or equal to 0.7 but less than 0.8; and (iv) “Poor” if thecalculated time efficiency may be less than 70%, or the calculatedcleaning performance may be less than 0.7. Further, the server 104 mayassociate any of various types of functional data for rating based on atype of the cleaning entity. For instance, for the cleaning entity beinga human cleaning staff operatively associated with the RCQM device 102,examples of such functional data may include, but not limited to,employment data (e.g., agent name, agent employee ID, designation,tenure, experience, previous organization, supervisor name, supervisoremployee ID, etc.), demographic data (for example, gender, race, age,education, accent, income, nationality, ethnicity, area code, zip code,marital status, job status, etc.), psychographic data (for example,introversion, sociability, aspirations, hobbies, etc.), system accessdata (for example, login ID, password, biometric data, etc.), and healthdata (e.g., existing and past medical conditions such as diabetes,hypertension, and heart stroke, existing and past medications, familyhistory of medical conditions, weight, etc. as well as lifestyle datasuch as exercise schedule, exercise amount, food habits, daily activityduration, and so on). In another instance, for the cleaning staff beinga cleaning equipment operatively associated with RCQM device 102,examples of the functional data may include, but not limited to, areacoverage, navigational and autonomous capabilities, manufacturer,equipment type, make and model, associated movable and non-movablecomponents, equipment dimensions (e.g., length, breadth, depth, height,area, etc.), equipment weight, communication abilities, and so on.

In another embodiment, the server 104 may be configured to applydifferent billing rates based on the calculated cleaning performanceand/or the time efficiency independently or in combination with otherparameters. For example, the server 104 may apply a relatively higherbilling rate if the calculated time efficiency may be greater than orequal to 90%, the calculated cleaning performance may be greater than orequal to 0.9, the performance rating of the cleaning entity may begreater than or equal to 90%, or the assessed cleaning quality may be“High quality,” or any combinations thereof.

In still another embodiment, the server 104 may be configured to conductremote cleaning inspections based on the calculated cleaning performanceand/or the time efficiency in independently or in combination with othercalculated parameters. For example, the server 104 may perform a remoteinspection for the determined position and define the assessed cleaningquality as (1) “Final—Work Complete” if the cleaning quality may beassessed as “High quality” and at least one of (i) the calculated timeefficiency may be greater than or equal to 70%, the calculated cleaningperformance may be greater than or equal to 0.7 and (2) “Needs Rework”if the cleaning quality may be assessed as “Low quality” or “Averagequality” and at least one of (i) the calculated time efficiency may beless than 70% and (ii) the calculated cleaning performance may be lessthan 0.7.

Other embodiments may include the server 104 being preconfigured ordynamically configured to (i) assess a cleaning task being performed and(ii) manage attendance of the cleaning entity at the designated physicallocation, or a portion thereof. For example, the server 104 may assess acleaning task being performed based on one or more inputs received froma suitable sensor located on the RCQM device 102, or an operativelyassociated user device 108 (e.g., cleaning equipment). For instance, theRCQM device 102 may include a vibration sensor providing vibration databased on the RCQM device 102 being moved. The server 104 may receive thevibration data to determine a cleaning task being performed. In anotherexample, the server 104 may manage an attendance of the cleaning entityat the designated physical location, or a portion thereof, based on thepreset period in the predefined cleaning schedule. For example, theserver 104 may be configured to automatically record a start clock timeand an end clock time based on the cleaning entity entering and leavingthe geofence area respectively. The recorded start and end clock timesmay be compared with preset clock times defining the preset period inthe predefined cleaning schedule to maintain a record of availability ofthe cleaning entity at the designated physical location, or a portionthereof. In some embodiments, the preset period may be defined by presetclock times for entering and exiting the indoor virtual fence.

The RCQM module 516 may communicate the signal plot plan, the assessedcleaning quality, each of the associated cumulative durations or otherdurations/periods and clock times, the calculated cleaning performance,and the calculated time efficiency to the output module 518 or storethem in the database 510 or the storage unit 508.

Output Module

The output module 518 may be in communication with various modulesincluding the input module 512, the plot training module 514, and theRCQM module 516, and the network devices such as the server 104 and thenetwork appliance 302. The output module 518 may receive or accessvarious data including the signal plot plan, the assessed cleaningquality, the calculated cumulative durations or other durations/periods,the calculated cleaning performance, and the calculated time efficiencyfrom the RCQM module 516, the database 510, or the storage unit 508.Examples of the output module 518 may include, but are not limited to, adisplay device such as a touchscreen display, a handling device such asa print head controller; a storage device such as the memory 506; anycomputing device such as a laptop, a mobile phone, a printer, and aserver 104; or any combination thereof.

In one embodiment, the output module 518 may be configured to store,process, communicate, display, or print the data. For example, theoutput module 518 may send the signal plot plan, the assessed cleaningquality, and the calculated cumulative duration spent to amultifunctional device including one or more printing or marking engines(not shown) configured to print the signal plot plan or a reportincluding the associated cumulative duration with, or without, thecleaning quality assessed using the server 104 in communication with theinput module 512. In some embodiments, the output module 518 may providean indication (e.g., audio, visual, haptic, text-based, symbolic, or anycombinations thereof) to a user or a computing device such as the userdevice 108 (e.g., a mobile phone, a desktop, etc.) accessible by a usersuch as a cleaning staff, a customer, and a supervisor. In some otherembodiments, the output module 518 may be provide an indication based onthe calculated cumulative duration exceeding a predefined time thresholdvalue (e.g., the first time threshold value 902-1, the second timethreshold value 902-2, the maximum time threshold value such as thethird time threshold value 902-3, etc.) in a set of one or morepredefined time threshold values such as the predefined time thresholdvalues 902. Other embodiments may include the output module 518providing an indication based on the assessed cleaning quality to assistin remotely managing the cleaning quality as well as the calculated timeefficiency and/or the cleaning performance of the cleaning entity, orany combinations thereof.

FIGS. 10-13 illustrate an exemplary application scenario forimplementing the RCQM device of FIG. 5, according to embodiments of thepresent disclosure. The application scenario is discussed herein withreference to the RCQM device 102; however, one having ordinary skill inthe art would understand such scenario including others may beimplemented with embodiments discussed above using RCQM devices 110 in adistributed or decentralized network architecture. In one embodiment,the RCQM device 102 may be implemented in two modes, namely, a trainingmode and an operation mode, to remotely manage the cleaning quality of adesignated cleaning location based on the cumulative duration spent at adesignated cleaning location based on a predefined cleaning schedule.

Training Mode

In one embodiment, the training mode may be performed in two temporallydistinct steps; however, one having ordinary skill in the art wouldunderstand that these steps may be combined for being performedsimultaneously. In a first step, the RCQM device 102 may receive thefloor plan and assign preliminary plot points therein to generate theplot plan via the input module 512. The RCQM device 102 may receive thefloor plan along with the set of predefined physical and non-physicalcharacteristics associated therewith and the one or more cleaningattributes. The floor plan may be received or accessed from the database510, the storage device such as the storage unit 508, or any othernetwork device. For example, as illustrated in FIG. 10, the RCQM device102 may access a floor plan 10 indicative of a designated physicallocation such as a storey of a building having a couple of rooms. Thefloor plan 10 may be associated with non-physical characteristics of thedesignated physical location such as room numbers, namely, “Room-X” and“Room-Y.” The floor plan 10 may also be associated with the physicalcharacteristics of the designated physical location. For example, thefloor plan 10 may be associated with image objects indicative of aboundary 12-1, 12-2, 12-3, and 12-4 (collectively, boundary 12), apartition 14, windows 16-1, 16-2, 16-3, and 16-4 (collectively, referredto as windows 16), entry/exit points 18-1, 18-2, and 18-3 (collectively,entry/exit points 18), and tangible objects such as fixed wirelessaccess points 20-1 and 20-2 (collectively, WAPs 20) associated withRoom-X. Similarly, the floor plan 10 may be further associated withimage objects indicative of a boundary 22-1, 22-2, 22-3, and 12-4, apartition 24, windows 26-1, 26-2, 26-3 (collectively, referred to aswindows 26), and entry/exit points 28-1, 28-2, 28-3, 18-2, as well asthe tangible objects such as WAPs 20 proximate to Room-Y. The dashedcurves may represent signals provided by the WAPs 20.

On the accessed floor plan 10, the RCQM device 102 may select a regionindicative of a portion of the designated physical location to becleaned. For example, the portion may indicate a designated cleaninglocation, which may be selected based on the one or more non-physicalcharacteristics of the designation physical location and/or the one ormore predefined cleaning attributes. For example, the input module 512of the RCQM device 102 may select one of the rooms on the floor plan 10based on the room name, “Room-X,” which may be required to be cleanedbased on the one or more cleaning attributes such as the predefinedcleaning schedule. In some embodiments, the region such as the Room-Xmay be selected by the RCQM device 102 based on a user input.

In the selected region such as Room-X, the RCQM device 102 may assign aset of preliminary plot points on the floor plan 10 to generate a plotplan 30. The input module 512 of the RCQM device 102 may assign the setof preliminary plot points based on the one or more associated physicalcharacteristics of the corresponding designated cleaning location. Inone example, as illustrated in FIG. 11, the RCQM device 102 may assign aset of preliminary plot points 32-1, 32-2, . . . , 32-n (collectivelyreferred to as preliminary plot points 32) based on the boundary 12 andthe partition 14 of the designated physical location indicated withinthe selected region of the floor plan 10. The input module 512 of theRCQM device 102 may identify the boundary 12 and the partition 14 usingany of a variety of computer vision and/or machine learning methodsknown in the art, related art, or developed later such as thosementioned above. Once identified, in one embodiment, the RCQM device 102may assign the preliminary plot points 32, shown as black circles, alongthe boundary 12 and the partition 14 with each of the preliminary plotpoints 32 at a shortest relative distance (e.g., at least approximately0.2 meters) from the boundary 12 and the partition 14 within Room-X. Inthe illustrated example of FIG. 11, the assigned preliminary plot points32 may substantially enclose the selected region, e.g., Room-X, of thefloor plan 10. Further, the RCQM device 102 may assign the preliminaryplot points 32 at a predefined distance (e.g., at least approximately0.3 meters) from each other to generate the plot plan 30.

In a second step of the training mode, the RCQM device 102 (indicated bya star in FIG. 12), or any other device in communication with the RCQMdevice 102 such as the user device 108, may be physically navigated, bya user or autonomously, through a portion of the designated physicallocation such as the designated cleaning location indicated by the plotplan 30. In some embodiments, the RCQM device 102 or the user device 108may be navigated autonomously using the processor(s) 502 or any othercontrol unit (not shown) in communication with the processor(s) 502. TheRCQM device 102 may assign one or more signal plot points on the plotplan 30 based on a predefined signal received by the RCQM device 102 atthe designated cleaning location such as Room-X. For example, asillustrated in FIG. 11, the plot training module 514 of the RCQM device102 may scan for Wi-Fi signals received from at least one fixed networkdevice such as the fixed WAPs 20 when the RCQM device 102 may beproximate to a portion of the designated physical location such asRoom-X. In some embodiments, the RCQM device 102 may include a proximitysensor, which may trigger the plot training module 514 to initiate thesignal scanning based on the RCQM device 102 being proximate to Room-X.The Wi-Fi signals may comprise of one or more signal samples, each beingin the for in of packets. Each signal sample may be associated with abasic service set identifier (BSSID) value indicative of the mediaaccess control (MAC) address of the WAPs 20, which may have generatedthat signal sample.

Further, the RCQM device 102 may determine the scanned Wi-Fi signal asbeing a stable signal if a predefined number of signal samples or pulses(e.g., at least two signal samples) are received for a predeterminedamount of time (e.g., one millisecond) from at least one of the fixedWAPs 20, provided signal strengths (e.g., RSSI values) of the Wi-Fisignal samples are above a predefined signal threshold value (e.g., −70dBm). Based on the stable Wi-Fi signals being received, the RCQM device102 may identify the WAPs 20 as available. The RCQM device 102 may thenbe navigated through various physical spots at the designated cleaninglocation such as Room-X to assign the one or more signal plot points onthe plot plan 30 based on the received stable Wi-Fi signals forgenerating a signal plot plan 40. Each signal plot point may beindicative of a physical spot in the designated physical location wherethe stable signal may be received. In one embodiment, the RCQM module516 may assign a signal plot point relative to a preliminary plot point.For example, as illustrated in FIG. 12, the plot training module 514 incommunication with the RCQM device 102 may assign the one or more signalplot points, shown as patterned squares, on the plot plan 30 to generatethe signal plot plan 40. The patterned squares may represent thephysical spots at the designated cleaning location (e.g., Room-X) suchthat (i) the physical spots may be within a predefined distance (e.g.,at least approximately one foot or approximately 0.3 meters) from theirnearest preliminary plot points, and (ii) the stable Wi-Fi signal may bereceived at those physical spots. Additionally, as shown in FIG. 12, theplot training module 514 may assign additional signal plot points withina predefined distance (e.g., at least approximately 0.5 meters) fromeach other and from those signal plot points that may be previouslyassigned proximate to the preliminary plot points. The signal plotpoints assigned proximate to the preliminary plot points, e.g., alongthe boundary 12 of the designated cleaning location, such as Room-X, maybe used as an indoor virtual fence for determining clock times when theRCQM device 102, or an operatively associated cleaning entity, enteredor exited Room-X. The narrow-dashed curves in FIG. 12 represent anexemplary physical path indicating the movement of the RCQM device 102,or a cleaning entity associated therewith, in the designated cleaninglocation for assigning the signal plot points. One of skill in the artwould understand that the RCQM device 102, or the cleaning entityassociated therewith, may be configured to follow any suitable path forassigning the signal plot points proximate to the preliminary plotpoints or otherwise as required.

Further, the preliminary plot points (not shown) may be assigned toobscured surfaces at the designated cleaning location based on a userinput. For example, the RCQM device 102 may assign the preliminary plotpoints on the floor plan 10 corresponding to locations at or around thetangible room objects, e.g., couch, television, game tables, etc.including the obscured surfaces proximate thereto. Examples of suchobscured surfaces may include, but are not limited to, a rear surface ofthe television, a floor surface underneath a couch, an underside surfaceof the game table, a constricted area behind a door, etc.

For each of the assigned signal plot points, the RCQM device 102 mayrecord a plot point identifier (e.g., a reference number, an indoorlocation coordinates, etc.) of the signal plot point, a signalidentifier such as the strength (e.g., RSSI value) of the stable Wi-Fisignal received at a physical spot indicated by the signal plot point, anetwork device identifier (e.g., BSSID value) of the WAPs 20 providingthe stable Wi-Fi signal for creating the training dataset. In someembodiments, the training dataset may also include geographical locationcoordinates associated with the designated physical location for each ofthe assigned signal plot points. The RCQM device 102 may accordinglycreate signal fingerprints of the stable Wi-Fi signal at the physicalspots in the designated cleaning location by way of mapping the signalplot points on the plot plan 30.

Operation Mode

During operation, a cleaning entity such as a user operativelyassociated with the RCQM device 102 may arrive to a geographicallocation such as an airport for an assigned work shift. The RCQM device102, in communication with the server 104, may identify the predefinedgeofence based on the GPS coordinates surrounding the geographicallocation stored in the database 510 or a storage unit such as thestorage unit 508. The server 104 may determine the availability of theuser for the assigned work shift as per the predefined cleaning scheduleat the geographical location based on the user entering the geofencearea. Upon determining the availability, the RCQM device 102 may recordthe clock time of arrival of the user and may scan for a nearest plotpoint at a designated physical location within the geographicallocation.

Further, the RCQM device 102 may access data associated with thedesignated physical location from the server 104 to assess the cleaningquality for a physical spot at the designated physical location or aportion thereof among other aspects. In some embodiments, such data aswell as the GPS coordinates defining the geofence area may be storedlocally in the database such as the database 510 of the RCQM device 102.The accessed data may include the training dataset, the signal plot plan40, and the cleaning attributes associated therewith corresponding to aportion of the designated physical location such as the designatedcleaning location to be cleaned. In one embodiment, in communicationwith the RCQM device 102, the server 104 may select at least one signalplot point based on the one or more cleaning attributes associated witha portion of the designated physical location such as the designatedcleaning location. For example, as shown in FIG. 13, the RCQM device 102may select the signal plot points, shown by black squares, proximate tothe windows 16 based on the predefined cleaning schedule. In oneinstance, the RCQM device 102, or the server 104, may select the signalplot points corresponding to physical spots within a predefined distance(e.g., at least approximately 1 meter) from the windows 16. In someembodiments, the signal plot points may be selected by the server 104based on a user input.

When the RCQM device 102 may be proximate to the designated cleaninglocation such as Room-X, the RCQM module 516 in communication with theRCQM device 102 may scan for a predefined signal such as Wi-Fi signalsreceived from at least one fixed network device, where both the receivedpredefined signal and the fixed network device may be used to create thetraining dataset for the designated cleaning location. If the receivedWi-Fi signals are stable signals, the RCQM device 102 may determine anattribute thereof such as the signal strength (e.g., represented by RSSIvalue) for determining a position of the RCQM device 102 relative to thesignal plot points such as the selected plot points. For example, theRCQM module 516 in communication with the RCQM device 102 may comparethe RSSI value of the received Wi-Fi signals with the RSSI values storedin the training dataset. Based on the comparison, the RCQM module 516may determine the position of the RCQM device 102 relative to the signalplot point whose corresponding stored RSSI value provides the shortestEuclidean distance with the RSSI value of the received Wi-Fi signal.Accordingly, the RCQM device 102 (indicated by a star in FIG. 13) may(i) record the entry and exit clock times based on the RCQM device 102being proximate to the signal plot points along the entry/exit points 18(e.g. doors) and the partition 14; (ii) a position of the RCQM device102 proximate to the selected signal plot points proximate to thewindows 16; (iii) determine a cumulative duration spent proximate to theselected signal plot points based on the predefined cleaning schedule;and (iv) determine the RCQM device 102 moving outside the proximity ofthe selected signal plot points based on the corresponding Euclideandistance exceeding a predefined distance threshold value.

Based on the determined cumulative duration, the server 104 may assessthe cleaning quality for the determined position. For example, theserver 104 may assess the cleaning quality as (1) “Low quality” if thecumulative duration spent by the RCQM device 102 may be less than orequal to a first time threshold value (e.g., 30 seconds); (2) “Averagequality” if the cumulative duration spent is less than or equal to asecond time threshold value (e.g., 60 seconds) and greater than thefirst time threshold value; or (3) “High quality” if the cumulativeduration is less than or equal to a third time threshold value (e.g. 120seconds), and greater than the second time threshold value. In oneembodiment, the third time threshold value may be the maximum durationin the predefined cleaning schedule. The maximum duration may refer tothe maximum amount of time available or set for completing an intendedcleaning task or a set of cleaning tasks at the determined positionproximate to a signal plot point such as the selected plot point. Thevalue of maximum duration may be defined or adjusted based on (i) thecleaning task and/or (ii) the designated physical location or a portionthereof associated with the predefined cleaning schedule. In someembodiments, the maximum duration may be less than or equal to thepreset period.

Further, the server 104 may consider a portion of the cumulativeduration as the unaccounted duration if the portion exceeds the maximumtime threshold value such as the third time threshold value 902-3. Theunaccounted duration may not be considered by the server 104 forassessing the cleaning quality. In some instances, the unaccountedduration may be indicative of a recess or a break time. In someembodiments, the cleaning quality may be further assessed based on (1) anumber of passes made by the RCQM device 102 proximate to the selectedsignal plot points, (2) a relative proximity of the RCQM device 102 tothe physical spots corresponding to the one or more selected signal plotpoints, and/or (3) such relative proximity while the RCQM device 102passes along a different physical spot corresponding to another signalplot point. In another embodiment, the RCQM device 102 may include apredefined time threshold value with respect to a specific unit of area.For instance, the RCQM device 102 may predefine or dynamically define atime threshold value per square meter to determine a cleaning quality asdiscussed above. In yet another embodiment, the RCQM device 102 mayassess the cleaning quality based on the total time spent by the RCQMmodule 516 at various designated cleaning locations or physical spotsindicated by the signal plot points on signal plot plan 40, as discussedabove.

In some embodiments, the RCQM device 102 may combine (1) surroundingimages or (2) orientation or direction sensor data of the RCQM device102, or that of an associated device such as the user device 108, orboth, with the determined cumulative duration to determine the cleaningquality. The computed data including the determined cumulative durationor other time periods (e.g., entry/exit clock times, a total time spentat a geographical location corresponding to the designated physicallocation, etc.), the assessed cleaning quality, the cleaningperformance, the time efficiency, and the signal plot plan 40 includingthe signal plot points traversed by the RCQM device 102 may becommunicated to a user via the interface(s) 504 or stored in thedatabase such as the database 510 or a storage device such as thestorage unit 508. Such data may be used for viewing, reporting, billing,performance management, inspections, cleaning quality management, or anyother suitable cleaning-related tasks for remotely managing the cleaningquality for a designated physical location, or any portion thereof suchas the designated cleaning location, in real-time. Additionally, theRCQM device 102 and/or the server 104 may send various indications tothe user, for example, at the end of the assigned work shift as per thepredefined cleaning schedule. Upon such indication or otherwise, theRCQM device 102 and/or the server 104 may send an alert to the userand/or a supervisor when the user leaves the geofence area indicative ofexiting the geographical location.

Although the above functions are performed by RCQM device 102, onehaving ordinary skill in the art would understand that aspects of theRCQM device 102 including the functions of the input module 512, theplot training module 514, or the RCQM module 516 may be executed by anetworked device such as the server 104, and vice versa, to assess ormanage the cleaning quality. Further, while aspects of the RCQM device102 are described in the context of a centralized model by way of aserver such as the server 104 operating in tandem with the RCQM device102, one having skilled in the art would understand that such aspectsmay be implemented through a decentralized, distributed networkarchitecture based on a blockchain methodology.

While the foregoing written description of the present disclosureenables one of ordinary skill to make and use what is consideredpresently to be the best mode thereof, those of ordinary skill willunderstand and appreciate the existence of variations, combinations, andequivalents of the specific embodiment, method, and examples herein. Thepresent disclosure should therefore not be limited by the abovedescribed embodiment, method, and examples, but by all embodiments andmethods within the scope and spirit of the present disclosure. Notably,the figures and examples are not meant to limit the scope of the presentdisclosure to a single embodiment, but other embodiments are possible byway of interchanging some or all of the described or illustratedelements.

1. A computer-implemented method for remotely managing a cleaningquality for an indoor location being cleaned, the method comprising:accessing, using a remote cleaning quality management (RCQM) module on acomputer with a processor and a memory, a training dataset including aplurality of plot points and one or more signal strengths associatedtherewith of a predefined signal received from at least one spatiallyfixed network device, the plurality of plot points corresponds tophysical spots at the indoor location being cleaned, wherein at leastone plot point is preselected from the plurality of plot points based ona predefined cleaning attribute associated with a physical spotcorresponding to the at least one plot point; receiving, using the RCQMmodule, the predefined signal at a position in the indoor location fromthe at least one spatially fixed network device, the received signalhaving a second signal strength greater than a predefined signalthreshold value, wherein the position is determined proximate to the atleast one plot point based on the second signal strength in combinationwith each of the one or more signal strengths; and calculating, usingthe RCQM module, a cumulative duration spent at the determined positionbased on a predefined cleaning schedule to assess a cleaning quality forthe physical spot, wherein the cleaning quality is assessed based on thecalculated cumulative duration being compared with a set of one or morepredefined time threshold values.
 2. The computer-implemented method ofclaim 1, further comprises: providing, using an output module on thecomputer in communication with the RCQM module, an indication based onthe calculated cumulative duration exceeding a predefined time thresholdvalue in the set of one or more predefined time threshold values.
 3. Thecomputer-implemented method of claim 1, wherein the set of one or morepredefined time threshold values is relative to a total time spentproximate to at least one of (i) the physical spot, (ii) the indoorlocation, (iii) a geographical location indicating the indoor location,and any combinations thereof.
 4. The computer-implemented method ofclaim 1, wherein each of the plurality of plot points is a virtualreference point associated with a floor plan of the indoor location,wherein at least one of the plurality of plot points is mapped on thefloor plan relative to one or more preliminary plot points, which arepreassigned to the floor plan based on physical characteristics of theindoor location, wherein the one or more preliminary plot points assistin defining a virtual fence proximate to the physical spot at the indoorlocation.
 5. The computer-implemented method of claim 1, wherein thecleaning schedule includes a predefined maximum duration for completinga cleaning task within a preset period, wherein the predefined maximumduration is less than the preset period.
 6. The computer-implementedmethod of claim 1, wherein the cleaning attribute includes at least oneof (i) the cleaning schedule, (ii) a cleaning task or a type thereof,(iii) a cleaning product, (iv) a cleaning equipment, (v) a proximity ofthe physical spot from a user or a predefined area proximate to theindoor location, (vi) a clock time, and any combinations thereof.
 7. Thecomputer-implemented method of claim 1, wherein the predefined signalthreshold value ranges from approximately −70 dBm to approximately −10dBm.
 8. The computer-implemented method of claim 1, wherein thepredefined signal corresponds to at least one of a radiofrequencysignal, a light signal, a sound signal, and any combinations thereof. 9.The computer-implemented method of claim 1, wherein the predefinedsignal is a Wi-Fi signal.
 10. The computer-implemented method of claim1, wherein the cumulative duration includes a single duration or a sumof at least two temporally separate durations.
 11. A system for remotelymanaging a cleaning quality for an indoor location being cleaned, thesystem comprising: a portable device capable of being navigated acrossone or more surfaces in the indoor location being cleaned, the portabledevice being configured to: access a training dataset including aplurality of plot points and one or more signal strengths associatedtherewith of a predefined signal received from at least one spatiallyfixed network device, wherein the plurality of plot points correspondsto physical spots at the indoor location; receive the predefined signalat a position in the indoor location from the at least one spatiallyfixed network device, wherein the received signal has a second signalstrength greater than a predefined signal threshold value; and calculatea cumulative duration at the position based on a predefined cleaningschedule; and a server in communication with the portable device, theserver being configured to: select at least one plot point from theplurality of plot points based on a predefined cleaning attributeassociated with a physical spot corresponding to the at least one plotpoint; determine the position being proximate to the selected at leastone plot point based on the second signal strength in combination witheach of the one or more signal strengths; and assess a cleaning qualityfor the physical spot based on the calculated cumulative duration at thedetermined position being compared with a set of one or more predefinedtime threshold values, wherein a portion of the calculated cumulativeduration exceeding a maximum time threshold value in the set isunaccounted towards assessing the cleaning quality.
 12. The system ofclaim 11, wherein the server is further configured to provide anindication based on the calculated cumulative duration exceeding themaximum time threshold value.
 13. The system of claim 11, wherein theset of one or more predefined time threshold values is relative to atotal time spent proximate to at least one of (i) the physical spot,(ii) the indoor location, (iii) a geographical location indicating theindoor location, and any combinations thereof.
 14. The system of claim11, wherein each of the plurality of plot points is a virtual referencepoint associated with a floor plan of the indoor location, wherein atleast one of the plurality of plot points is mapped on the floor planrelative to one or more preliminary plot points, which are preassignedto the floor plan based on physical characteristics of the indoorlocation, wherein the one or more preliminary plot points assist indefining a virtual fence proximate to the physical spot at the indoorlocation.
 15. The system of claim 11, wherein the cleaning scheduleincludes a predefined maximum duration for completing a cleaning taskwithin a preset period, wherein the predefined maximum duration is lessthan the preset period.
 16. The system of claim 11, wherein the cleaningattribute includes at least one of (i) the cleaning schedule, (ii) acleaning task or a type thereof, (iii) a cleaning product, (iv) acleaning equipment, (v) a proximity of the physical spot from a user ora predefined area proximate to the indoor location, (vi) a clock time,and any combinations thereof.
 17. The system of claim 11, wherein thepredefined signal threshold value ranges from approximately −70 dBm toapproximately −10 dBm.
 18. The system of claim 11, wherein thepredefined signal corresponds to at least one of a radiofrequencysignal, a light signal, a sound signal, and any combinations thereof.19. The system of claim 11, wherein the predefined signal is a Wi-Fisignal.
 20. The system of claim 11, wherein the cumulative durationincludes a single duration or a sum of at least two temporally separatedurations.